Container Recovery Layer Prioritization

ABSTRACT

An illustrative method of container recovery using layer prioritization includes identifying a set of immutable layers of container images included in a dataset used by a container system to run containerized applications on a first cluster; copying the set of immutable layers of container images to a second cluster in preparation for a recovery event; receiving, after the set of immutable layers of container images are copied to the second cluster, a recovery request to recover the containerized applications; and copying, in response to the recovery request, a set of mutable layers included in the dataset to the second cluster, the second cluster configured to use the copied set of immutable layers and the copied set of mutable layers to recover the containerized applications on the second cluster.

RELATED APPLICATIONS

The present application is a continuation-in-part application of U.S.patent application Ser. No. 17/733,716 filed on Apr. 29, 2022, which isa continuation-in-part application of U.S. patent application Ser. No.17/580,098 filed on Jan. 20, 2022, each of which is expresslyincorporated by reference herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various embodiments and are a partof the specification. The illustrated embodiments are merely examplesand do not limit the scope of the disclosure. Throughout the drawings,identical or similar reference numbers designate identical or similarelements.

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 3C sets forth an example of a cloud-based storage system inaccordance with some embodiments of the present disclosure.

FIG. 3D illustrates an exemplary computing device that may bespecifically configured to perform one or more of the processesdescribed herein.

FIG. 3E illustrates an example of a fleet of storage systems forproviding storage services in accordance with some embodiments of thepresent disclosure.

FIG. 4 illustrates an example container system in accordance with someembodiments of the present disclosure.

FIG. 5 illustrates an example configuration of a container system and acontainer-aware storage system in accordance with some embodiments ofthe present disclosure.

FIG. 6 illustrates an example of a container-aware storage systemreceiving a container image and storing the container image as a volumein accordance with some embodiments of the present disclosure.

FIGS. 7A-7B illustrate examples of a container-aware storage systemproviding volumes to a container system in accordance with someembodiments of the present disclosure.

FIGS. 8-11 illustrate flowcharts depicting example methods in accordancewith some embodiments of the present disclosure.

FIGS. 12A-15 illustrate examples of using data volumes to storecontainer images in accordance with some embodiments of the presentdisclosure.

FIGS. 15-18 illustrate flowcharts depicting example methods inaccordance with some embodiments of the present disclosure.

FIGS. 19A-19B illustrate computing environments depicting containerrecovery using volumes comprising container images in accordance withsome embodiments of the present disclosure.

FIGS. 20-21 illustrate flowcharts depicting example methods inaccordance with some embodiments of the present disclosure.

FIGS. 22A-22E illustrate an example configuration in which a recoveryprocess is applied in accordance with some embodiments of the presentdisclosure.

FIGS. 23-25 illustrate example container recovery methods in accordancewith some embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, systems, apparatus, and products for container recoverylayer prioritization in accordance with embodiments of the presentdisclosure are described with reference to the accompanying drawings,beginning with FIG. 1A. FIG. 1A illustrates an example system for datastorage, in accordance with some implementations. System 100 (alsoreferred to as “storage system” herein) includes numerous elements forpurposes of illustration rather than limitation. It may be noted thatsystem 100 may include the same, more, or fewer elements configured inthe same or different manner in other implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (‘IP’), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include mechanical or spinning hard disk, such ashard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may be similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an ASIC, an FPGA, adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

In implementations, storage drive 171A-F may be one or more zonedstorage devices. In some implementations, the one or more zoned storagedevices may be a shingled HDD. In implementations, the one or morestorage devices may be a flash-based SSD. In a zoned storage device, azoned namespace on the zoned storage device can be addressed by groupsof blocks that are grouped and aligned by a natural size, forming anumber of addressable zones. In implementations utilizing an SSD, thenatural size may be based on the erase block size of the SSD. In someimplementations, the zones of the zoned storage device may be definedduring initialization of the zoned storage device. In implementations,the zones may be defined dynamically as data is written to the zonedstorage device.

In some implementations, zones may be heterogeneous, with some zoneseach being a page group and other zones being multiple page groups. Inimplementations, some zones may correspond to an erase block and otherzones may correspond to multiple erase blocks. In an implementation,zones may be any combination of differing numbers of pages in pagegroups and/or erase blocks, for heterogeneous mixes of programmingmodes, manufacturers, product types and/or product generations ofstorage devices, as applied to heterogeneous assemblies, upgrades,distributed storages, etc. In some implementations, zones may be definedas having usage characteristics, such as a property of supporting datawith particular kinds of longevity (very short lived or very long lived,for example). These properties could be used by a zoned storage deviceto determine how the zone will be managed over the zone's expectedlifetime.

It should be appreciated that a zone is a virtual construct. Anyparticular zone may not have a fixed location at a storage device. Untilallocated, a zone may not have any location at a storage device. A zonemay correspond to a number representing a chunk of virtually allocatablespace that is the size of an erase block or other block size in variousimplementations. When the system allocates or opens a zone, zones getallocated to flash or other solid-state storage memory and, as thesystem writes to the zone, pages are written to that mapped flash orother solid-state storage memory of the zoned storage device. When thesystem closes the zone, the associated erase block(s) or other sizedblock(s) are completed. At some point in the future, the system maydelete a zone which will free up the zone's allocated space. During itslifetime, a zone may be moved around to different locations of the zonedstorage device, e.g., as the zoned storage device does internalmaintenance.

In implementations, the zones of the zoned storage device may be indifferent states. A zone may be in an empty state in which data has notbeen stored at the zone. An empty zone may be opened explicitly, orimplicitly by writing data to the zone. This is the initial state forzones on a fresh zoned storage device, but may also be the result of azone reset. In some implementations, an empty zone may have a designatedlocation within the flash memory of the zoned storage device. In animplementation, the location of the empty zone may be chosen when thezone is first opened or first written to (or later if writes arebuffered into memory). A zone may be in an open state either implicitlyor explicitly, where a zone that is in an open state may be written tostore data with write or append commands. In an implementation, a zonethat is in an open state may also be written to using a copy commandthat copies data from a different zone. In some implementations, a zonedstorage device may have a limit on the number of open zones at aparticular time.

A zone in a closed state is a zone that has been partially written to,but has entered a closed state after issuing an explicit closeoperation. A zone in a closed state may be left available for futurewrites, but may reduce some of the run-time overhead consumed by keepingthe zone in an open state. In implementations, a zoned storage devicemay have a limit on the number of closed zones at a particular time. Azone in a full state is a zone that is storing data and can no longer bewritten to. A zone may be in a full state either after writes havewritten data to the entirety of the zone or as a result of a zone finishoperation. Prior to a finish operation, a zone may or may not have beencompletely written. After a finish operation, however, the zone may notbe opened a written to further without first performing a zone resetoperation.

The mapping from a zone to an erase block (or to a shingled track in anHDD) may be arbitrary, dynamic, and hidden from view. The process ofopening a zone may be an operation that allows a new zone to bedynamically mapped to underlying storage of the zoned storage device,and then allows data to be written through appending writes into thezone until the zone reaches capacity. The zone can be finished at anypoint, after which further data may not be written into the zone. Whenthe data stored at the zone is no longer needed, the zone can be resetwhich effectively deletes the zone's content from the zoned storagedevice, making the physical storage held by that zone available for thesubsequent storage of data. Once a zone has been written and finished,the zoned storage device ensures that the data stored at the zone is notlost until the zone is reset. In the time between writing the data tothe zone and the resetting of the zone, the zone may be moved aroundbetween shingle tracks or erase blocks as part of maintenance operationswithin the zoned storage device, such as by copying data to keep thedata refreshed or to handle memory cell aging in an SSD.

In implementations utilizing an HDD, the resetting of the zone may allowthe shingle tracks to be allocated to a new, opened zone that may beopened at some point in the future. In implementations utilizing an SSD,the resetting of the zone may cause the associated physical eraseblock(s) of the zone to be erased and subsequently reused for thestorage of data. In some implementations, the zoned storage device mayhave a limit on the number of open zones at a point in time to reducethe amount of overhead dedicated to keeping zones open.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (PCP) flash storage device 118 with separately addressablefast write storage. System 117 may include a storage device controller119. In one embodiment, storage device controller 119A-D may be a CPU,ASIC, FPGA, or any other circuitry that may implement control structuresnecessary according to the present disclosure. In one embodiment, system117 includes flash memory devices (e.g., including flash memory devices120 a-n), operatively coupled to various channels of the storage devicecontroller 119. Flash memory devices 120 a-n, may be presented to thecontroller 119A-D as an addressable collection of Flash pages, eraseblocks, and/or control elements sufficient to allow the storage devicecontroller 119A-D to program and retrieve various aspects of the Flash.In one embodiment, storage device controller 119A-D may performoperations on flash memory devices 120 a-n including storing andretrieving data content of pages, arranging and erasing any blocks,tracking statistics related to the use and reuse of Flash memory pages,erase blocks, and cells, tracking and predicting error codes and faultswithin the Flash memory, controlling voltage levels associated withprogramming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 117 may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices120 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the stored energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example storage system 124 for data storagein accordance with some implementations. In one embodiment, storagesystem 124 includes storage controllers 125 a, 125b. In one embodiment,storage controllers 125 a, 125 b are operatively coupled to Dual PCIstorage devices. Storage controllers 125 a, 125 b may be operativelycoupled (e.g., via a storage network 130) to some number of hostcomputers 127a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126a-d) to host computers 127a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices119a-d to journal in progress operationsto ensure the operations are not lost on a power failure, storagecontroller removal, storage controller or storage system shutdown, orsome fault of one or more software or hardware components within thestorage system 124.

In one embodiment, storage controllers 125 a, 125 b operate as PCImasters to one or the other PCI buses 128 a, 128b. In anotherembodiment, 128a and 128b may be based on other communications standards(e.g., HyperTransport, InfiniBand, etc.). Other storage systemembodiments may operate storage controllers 125 a, 125 b asmulti-masters for both PCI buses 128 a, 128b. Alternately, aPCI/NVMe/NVMf switching infrastructure or fabric may connect multiplestorage controllers. Some storage system embodiments may allow storagedevices to communicate with each other directly rather thancommunicating only with storage controllers. In one embodiment, astorage device controller 119a may be operable under direction from astorage controller 125 a to synthesize and transfer data to be storedinto Flash memory devices from data that has been stored in RAM (e.g.,RAM 121 of FIG. 1C). For example, a recalculated version of RAM contentmay be transferred after a storage controller has determined that anoperation has fully committed across the storage system, or whenfast-write memory on the device has reached a certain used capacity, orafter a certain amount of time, to ensure improve safety of the data orto release addressable fast-write capacity for reuse. This mechanism maybe used, for example, to avoid a second transfer over a bus (e.g., 128a, 128 b) from the storage controllers 125 a, 125b. In one embodiment, arecalculation may include compressing data, attaching indexing or othermetadata, combining multiple data segments together, performing erasurecode calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one storage controller 125 a to another storage controller 125b, or it could be used to offload compression, data aggregation, and/orerasure coding calculations and transfers to storage devices to reduceload on storage controllers or the storage controller interface 129 a,129 b to the PCI bus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storage152 units or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storage 152, for exampleas lists or other data structures stored in memory. In some embodimentsthe authorities are stored within the non-volatile solid state storage152 and supported by software executing on a controller or otherprocessor of the non-volatile solid state storage 152. In a furtherembodiment, authorities 168 are implemented on the storage nodes 150,for example as lists or other data structures stored in the memory 154and supported by software executing on the CPU 156 of the storage node150. Authorities 168 control how and where data is stored in thenon-volatile solid state storage 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storage 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIG. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In embodiments, authorities 168 operate to determine how operations willproceed against particular logical elements. Each of the logicalelements may be operated on through a particular authority across aplurality of storage controllers of a storage system. The authorities168 may communicate with the plurality of storage controllers so thatthe plurality of storage controllers collectively perform operationsagainst those particular logical elements.

In embodiments, logical elements could be, for example, files,directories, object buckets, individual objects, delineated parts offiles or objects, other forms of key-value pair databases, or tables. Inembodiments, performing an operation can involve, for example, ensuringconsistency, structural integrity, and/or recoverability with otheroperations against the same logical element, reading metadata and dataassociated with that logical element, determining what data should bewritten durably into the storage system to persist any changes for theoperation, or where metadata and data can be determined to be storedacross modular storage devices attached to a plurality of the storagecontrollers in the storage system.

In some embodiments the operations are token based transactions toefficiently communicate within a distributed system. Each transactionmay be accompanied by or associated with a token, which gives permissionto execute the transaction. The authorities 168 are able to maintain apre-transaction state of the system until completion of the operation insome embodiments. The token based communication may be accomplishedwithout a global lock across the system, and also enables restart of anoperation in case of a disruption or other failure.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Inodes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage 152 unit may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.,multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/0) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., an FPGA.In this embodiment, each flash die 222 has pages, organized as sixteenkB (kilobyte) pages 224, and a register 226 through which data can bewritten to or read from the flash die 222. In further embodiments, othertypes of solid-state memory are used in place of, or in addition toflash memory illustrated within flash die 222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The non-volatile solid statestorage 152 units described herein have multiple interfaces activesimultaneously and serving multiple purposes. In some embodiments, someof the functionality of a storage node 150 is shifted into a storageunit 152, transforming the storage unit 152 into a combination ofstorage unit 152 and storage node 150. Placing computing (relative tostorage data) into the storage unit 152 places this computing closer tothe data itself. The various system embodiments have a hierarchy ofstorage node layers with different capabilities. By contrast, in astorage array, a controller owns and knows everything about all of thedata that the controller manages in a shelf or storage devices. In astorage cluster 161, as described herein, multiple controllers inmultiple non-volatile sold state storage 152 units and/or storage nodes150 cooperate in various ways (e.g., for erasure coding, data sharding,metadata communication and redundancy, storage capacity expansion orcontraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage 152 units of FIGS. 2A-C. In thisversion, each non-volatile solid state storage 152 unit has a processorsuch as controller 212 (see FIG. 2C), an FPGA, flash memory 206, andNVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS. 2B and2C) on a PCIe (peripheral component interconnect express) board in achassis 138 (see FIG. 2A). The non-volatile solid state storage 152 unitmay be implemented as a single board containing storage, and may be thelargest tolerable failure domain inside the chassis. In someembodiments, up to two non-volatile solid state storage 152 units mayfail and the device will continue with no data loss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the non-volatile solid state storage 152 DRAM 216,and is backed by NAND flash. NVRAM 204 is logically divided intomultiple memory regions written for two as spool (e.g., spool region).Space within the NVRAM 204 spools is managed by each authority 168independently. Each device provides an amount of storage space to eachauthority 168. That authority 168 further manages lifetimes andallocations within that space. Examples of a spool include distributedtransactions or notions. When the primary power to a non-volatile solidstate storage 152 unit fails, onboard super-capacitors provide a shortduration of power hold up. During this holdup interval, the contents ofthe NVRAM 204 are flushed to flash memory 206. On the next power-on, thecontents of the NVRAM 204 are recovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources. In one embodiment, the compute plane256 may perform the operations of a storage array controller, asdescribed herein, on one or more devices of the storage plane 258 (e.g.,a storage array).

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g., partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or LAN, or as some other mechanism capable oftransporting digital information between the storage system 306 and thecloud services provider 302. Such a data communications link 304 may befully wired, fully wireless, or some aggregation of wired and wirelessdata communications pathways. In such an example, digital informationmay be exchanged between the storage system 306 and the cloud servicesprovider 302 via the data communications link 304 using one or more datacommunications protocols. For example, digital information may beexchanged between the storage system 306 and the cloud services provider302 via the data communications link 304 using the handheld devicetransfer protocol (‘HDTP’), hypertext transfer protocol (‘HTTP’),interne protocol (‘IP’), real-time transfer protocol (‘RTP’),transmission control protocol (‘TCP’), user datagram protocol (‘UDP’),wireless application protocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides a vastarray of services to users of the cloud services provider 302 throughthe sharing of computing resources via the data communications link 304.The cloud services provider 302 may provide on-demand access to a sharedpool of configurable computing resources such as computer networks,servers, storage, applications and services, and so on. The shared poolof configurable resources may be rapidly provisioned and released to auser of the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services through the implementation of aninfrastructure as a service (‘IaaS’) service model, through theimplementation of a platform as a service (‘PaaS’) service model,through the implementation of a software as a service (‘SaaS’) servicemodel, through the implementation of an authentication as a service(‘AaaS’) service model, through the implementation of a storage as aservice model where the cloud services provider 302 offers access to itsstorage infrastructure for use by the storage system 306 and users ofthe storage system 306, and so on. Readers will appreciate that thecloud services provider 302 may be configured to provide additionalservices to the storage system 306 and users of the storage system 306through the implementation of additional service models, as the servicemodels described above are included only for explanatory purposes and inno way represent a limitation of the services that may be offered by thecloud services provider 302 or a limitation as to the service modelsthat may be implemented by the cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. In still alternative embodiments,the cloud services provider 302 may be embodied as a mix of a privateand public cloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat a vast amount of additional hardware components and additionalsoftware components may be necessary to facilitate the delivery of cloudservices to the storage system 306 and users of the storage system 306.For example, the storage system 306 may be coupled to (or even include)a cloud storage gateway. Such a cloud storage gateway may be embodied,for example, as hardware-based or software-based appliance that islocated on premise with the storage system 306. Such a cloud storagegateway may operate as a bridge between local applications that areexecuting on the storage system 306 and remote, cloud-based storage thatis utilized by the storage system 306. Through the use of a cloudstorage gateway, organizations may move primary iSCSI or NAS to thecloud services provider 302, thereby enabling the organization to savespace on their on-premises storage systems. Such a cloud storage gatewaymay be configured to emulate a disk array, a block-based device, a fileserver, or other storage system that can translate the SCSI commands,file server commands, or other appropriate command into REST-spaceprotocols that facilitate communications with the cloud servicesprovider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model, eliminating the need to install and run theapplication on local computers, which may simplify maintenance andsupport of the application. Such applications may take many forms inaccordance with various embodiments of the present disclosure. Forexample, the cloud services provider 302 may be configured to provideaccess to data analytics applications to the storage system 306 andusers of the storage system 306. Such data analytics applications may beconfigured, for example, to receive vast amounts of telemetry dataphoned home by the storage system 306. Such telemetry data may describevarious operating characteristics of the storage system 306 and may beanalyzed for a vast array of purposes including, for example, todetermine the health of the storage system 306, to identify workloadsthat are executing on the storage system 306, to predict when thestorage system 306 will run out of various resources, to recommendconfiguration changes, hardware or software upgrades, workflowmigrations, or other actions that may improve the operation of thestorage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

Although the example depicted in FIG. 3A illustrates the storage system306 being coupled for data communications with the cloud servicesprovider 302, in other embodiments the storage system 306 may be part ofa hybrid cloud deployment in which private cloud elements (e.g., privatecloud services, on-premises infrastructure, and so on) and public cloudelements (e.g., public cloud services, infrastructure, and so on thatmay be provided by one or more cloud services providers) are combined toform a single solution, with orchestration among the various platforms.Such a hybrid cloud deployment may leverage hybrid cloud managementsoftware such as, for example, Azure™ Arc from Microsoft™, thatcentralize the management of the hybrid cloud deployment to anyinfrastructure and enable the deployment of services anywhere. In suchan example, the hybrid cloud management software may be configured tocreate, update, and delete resources (both physical and virtual) thatform the hybrid cloud deployment, to allocate compute and storage tospecific workloads, to monitor workloads and resources for performance,policy compliance, updates and patches, security status, or to perform avariety of other tasks.

Readers will appreciate that by pairing the storage systems describedherein with one or more cloud services providers, various offerings maybe enabled. For example, disaster recovery as a service (‘DRaaS’) may beprovided where cloud resources are utilized to protect applications anddata from disruption caused by disaster, including in embodiments wherethe storage systems may serve as the primary data store. In suchembodiments, a total system backup may be taken that allows for businesscontinuity in the event of system failure. In such embodiments, clouddata backup techniques (by themselves or as part of a larger DRaaSsolution) may also be integrated into an overall solution that includesthe storage systems and cloud services providers described herein.

The storage systems described herein, as well as the cloud servicesproviders, may be utilized to provide a wide array of security features.For example, the storage systems may encrypt data at rest (and data maybe sent to and from the storage systems encrypted) and may make use ofKey Management-as-a-Service (‘KMaaS’) to manage encryption keys, keysfor locking and unlocking storage devices, and so on. Likewise, clouddata security gateways or similar mechanisms may be utilized to ensurethat data stored within the storage systems does not improperly end upbeing stored in the cloud as part of a cloud data backup operation.Furthermore, microsegmentation or identity-based-segmentation may beutilized in a data center that includes the storage systems or withinthe cloud services provider, to create secure zones in data centers andcloud deployments that enables the isolation of workloads from oneanother.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include a vast amount ofstorage resources 308, which may be embodied in many forms. For example,the storage resources 308 can include nano-RAM or another form ofnonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate, 3D crosspoint non-volatile memory, flashmemory including single-level cell (‘SLC’) NAND flash, multi-level cell(‘MLC’) NAND flash, triple-level cell (‘TLC’) NAND flash, quad-levelcell (‘OLC’) NAND flash, or others. Likewise, the storage resources 308may include non-volatile magnetoresistive random-access memory (‘MRAM’),including spin transfer torque (‘STT’) MRAM. The example storageresources 308 may alternatively include non-volatile phase-change memory(‘PCM’), quantum memory that allows for the storage and retrieval ofphotonic quantum information, resistive random-access memory (‘ReRAM’),storage class memory (‘SCM’), or other form of storage resources,including any combination of resources described herein. Readers willappreciate that other forms of computer memories and storage devices maybe utilized by the storage systems described above, including DRAM,SRAM, EEPROM, universal memory, and many others. The storage resources308 depicted in FIG. 3A may be embodied in a variety of form factors,including but not limited to, dual in-line memory modules (‘DIMMs’),non-volatile dual in-line memory modules (‘NVDIMMs’), M.2, U.2, andothers.

The storage resources 308 depicted in FIG. 3B may include various formsof SCM. SCM may effectively treat fast, non-volatile memory (e.g., NANDflash) as an extension of DRAM such that an entire dataset may betreated as an in-memory dataset that resides entirely in DRAM. SCM mayinclude non-volatile media such as, for example, NAND flash. Such NANDflash may be accessed utilizing NVMe that can use the PCIe bus as itstransport, providing for relatively low access latencies compared toolder protocols. In fact, the network protocols used for SSDs inall-flash arrays can include NVMe using Ethernet (ROCE, NVME TCP), FibreChannel (NVMe FC), InfiniBand (iWARP), and others that make it possibleto treat fast, non-volatile memory as an extension of DRAM. In view ofthe fact that DRAM is often byte-addressable and fast, non-volatilememory such as NAND flash is block-addressable, a controllersoftware/hardware stack may be needed to convert the block data to thebytes that are stored in the media. Examples of media and software thatmay be used as SCM can include, for example, 3D XPoint, Intel MemoryDrive Technology, Samsung's Z-SSD, and others.

The storage resources 308 depicted in FIG. 3B may also include racetrackmemory (also referred to as domain-wall memory). Such racetrack memorymay be embodied as a form of non-volatile, solid-state memory thatrelies on the intrinsic strength and orientation of the magnetic fieldcreated by an electron as it spins in addition to its electronic charge,in solid-state devices. Through the use of spin-coherent electriccurrent to move magnetic domains along a nanoscopic permalloy wire, thedomains may pass by magnetic read/write heads positioned near the wireas current is passed through the wire, which alter the domains to recordpatterns of bits. In order to create a racetrack memory device, manysuch wires and read/write elements may be packaged together.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The example storage system 306 depicted in FIG. 3B may leverage thestorage resources described above in a variety of different ways. Forexample, some portion of the storage resources may be utilized to serveas a write cache, storage resources within the storage system may beutilized as a read cache, or tiering may be achieved within the storagesystems by placing data within the storage system in accordance with oneor more tiering policies.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306, including embodiments where thoseresources are separated by a relatively vast expanse. The communicationsresources 310 may be configured to utilize a variety of differentprotocols and data communication fabrics to facilitate datacommunications between components within the storage systems as well ascomputing devices that are outside of the storage system. For example,the communications resources 310 can include fibre channel (‘FC’)technologies such as FC fabrics and FC protocols that can transport SCSIcommands over FC network, FC over ethernet (‘FCoE’) technologies throughwhich FC frames are encapsulated and transmitted over Ethernet networks,InfiniBand (‘IB’) technologies in which a switched fabric topology isutilized to facilitate transmissions between channel adapters, NVMExpress (‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’)technologies through which non-volatile storage media attached via a PCIexpress (‘PCIe’) bus may be accessed, and others. In fact, the storagesystems described above may, directly or indirectly, make use ofneutrino communication technologies and devices through whichinformation (including binary information) is transmitted using a beamof neutrinos.

The communications resources 310 can also include mechanisms foraccessing storage resources 308 within the storage system 306 utilizingserial attached SCSI (‘SAS’), serial ATA (‘SATA’) bus interfaces forconnecting storage resources 308 within the storage system 306 to hostbus adapters within the storage system 306, internet small computersystems interface (‘iSCSI’) technologies to provide block-level accessto storage resources 308 within the storage system 306, and othercommunications resources that that may be useful in facilitating datacommunications between components within the storage system 306, as wellas data communications between the storage system 306 and computingdevices that are outside of the storage system 306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or more ASICsthat are customized for some particular purpose as well as one or moreCPUs. The processing resources 312 may also include one or more DSPs,one or more FPGAs, one or more systems on a chip (‘ SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform a vast array of tasks. The softwareresources 314 may include, for example, one or more modules of computerprogram instructions that when executed by processing resources 312within the storage system 306 are useful in carrying out various dataprotection techniques. Such data protection techniques may be carriedout, for example, by system software executing on computer hardwarewithin the storage system, by a cloud services provider, or in otherways. Such data protection techniques can include data archiving, databackup, data replication, data snapshotting, data and database cloning,and other data protection techniques.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage system 306. For example, the software resources 314 may includesoftware modules that perform various data reduction techniques such as,for example, data compression, data deduplication, and others. Thesoftware resources 314 may include software modules that intelligentlygroup together I/O operations to facilitate better usage of theunderlying storage resource 308, software modules that perform datamigration operations to migrate from within a storage system, as well assoftware modules that perform other functions. Such software resources314 may be embodied as one or more software containers or in many otherways.

For further explanation, FIG. 3C sets forth an example of a cloud-basedstorage system 318 in accordance with some embodiments of the presentdisclosure. In the example depicted in FIG. 3C, the cloud-based storagesystem 318 is created entirely in a cloud computing environment 316 suchas, for example, Amazon Web Services (‘AWS’)™, Microsoft Azure™, GoogleCloud Platform™, IBM Cloud™, Oracle Cloud™, and others. The cloud-basedstorage system 318 may be used to provide services similar to theservices that may be provided by the storage systems described above.

The cloud-based storage system 318 depicted in FIG. 3C includes twocloud computing instances 320, 322 that each are used to support theexecution of a storage controller application 324, 326. The cloudcomputing instances 320, 322 may be embodied, for example, as instancesof cloud computing resources (e.g., virtual machines) that may beprovided by the cloud computing environment 316 to support the executionof software applications such as the storage controller application 324,326. For example, each of the cloud computing instances 320, 322 mayexecute on an Azure VM, where each Azure VM may include high speedtemporary storage that may be leveraged as a cache (e.g., as a readcache). In one embodiment, the cloud computing instances 320, 322 may beembodied as Amazon Elastic Compute Cloud (‘EC2’) instances. In such anexample, an Amazon Machine Image (AMP) that includes the storagecontroller application 324, 326 may be booted to create and configure avirtual machine that may execute the storage controller application 324,326.

In the example method depicted in FIG. 3C, the storage controllerapplication 324, 326 may be embodied as a module of computer programinstructions that, when executed, carries out various storage tasks. Forexample, the storage controller application 324, 326 may be embodied asa module of computer program instructions that, when executed, carriesout the same tasks as the controllers 110A, 110B in FIG. 1A describedabove such as writing data to the cloud-based storage system 318,erasing data from the cloud-based storage system 318, retrieving datafrom the cloud-based storage system 318, monitoring and reporting ofdisk utilization and performance, performing redundancy operations, suchas RAID or RAID-like data redundancy operations, compressing data,encrypting data, deduplicating data, and so forth. Readers willappreciate that because there are two cloud computing instances 320, 322that each include the storage controller application 324, 326, in someembodiments one cloud computing instance 320 may operate as the primarycontroller as described above while the other cloud computing instance322 may operate as the secondary controller as described above. Readerswill appreciate that the storage controller application 324, 326depicted in FIG. 3C may include identical source code that is executedwithin different cloud computing instances 320, 322 such as distinct EC2instances.

Readers will appreciate that other embodiments that do not include aprimary and secondary controller are within the scope of the presentdisclosure. For example, each cloud computing instance 320, 322 mayoperate as a primary controller for some portion of the address spacesupported by the cloud-based storage system 318, each cloud computinginstance 320, 322 may operate as a primary controller where theservicing of I/O operations directed to the cloud-based storage system318 are divided in some other way, and so on. In fact, in otherembodiments where costs savings may be prioritized over performancedemands, only a single cloud computing instance may exist that containsthe storage controller application.

The cloud-based storage system 318 depicted in FIG. 3C includes cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. The cloud computing instances 340 a, 340 b, 340 n may be embodied,for example, as instances of cloud computing resources that may beprovided by the cloud computing environment 316 to support the executionof software applications. The cloud computing instances 340 a, 340 b,340 n of FIG. 3C may differ from the cloud computing instances 320, 322described above as the cloud computing instances 340a, 340 b, 340 n ofFIG. 3C have local storage 330, 334, 338 resources whereas the cloudcomputing instances 320, 322 that support the execution of the storagecontroller application 324, 326 need not have local storage resources.The cloud computing instances 340 a, 340 b, 340 n with local storage330, 334, 338 may be embodied, for example, as EC2 M5 instances thatinclude one or more SSDs, as EC2 R5 instances that include one or moreSSDs, as EC2 I3 instances that include one or more SSDs, and so on. Insome embodiments, the local storage 330, 334, 338 must be embodied assolid-state storage (e.g., SSDs) rather than storage that makes use ofhard disk drives.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 caninclude a software daemon 328, 332, 336 that, when executed by a cloudcomputing instance 340 a, 340 b, 340 n can present itself to the storagecontroller applications 324, 326 as if the cloud computing instance 340a, 340 b, 340 n were a physical storage device (e.g., one or more SSDs).In such an example, the software daemon 328, 332, 336 may includecomputer program instructions similar to those that would normally becontained on a storage device such that the storage controllerapplications 324, 326 can send and receive the same commands that astorage controller would send to storage devices. In such a way, thestorage controller applications 324, 326 may include code that isidentical to (or substantially identical to) the code that would beexecuted by the controllers in the storage systems described above. Inthese and similar embodiments, communications between the storagecontroller applications 324, 326 and the cloud computing instances 340a, 340 b, 340n with local storage 330, 334, 338 may utilize iSCSI, NVMeover TCP, messaging, a custom protocol, or in some other mechanism.

In the example depicted in FIG. 3C, each of the cloud computinginstances 340 a, 340 b, 340 n with local storage 330, 334, 338 may alsobe coupled to block storage 342, 344, 346 that is offered by the cloudcomputing environment 316 such as, for example, as Amazon Elastic BlockStore (‘EBS’) volumes. In such an example, the block storage 342, 344,346 that is offered by the cloud computing environment 316 may beutilized in a manner that is similar to how the NVRAM devices describedabove are utilized, as the software daemon 328, 332, 336 (or some othermodule) that is executing within a particular cloud comping instance 340a, 340 b, 340 n may, upon receiving a request to write data, initiate awrite of the data to its attached EBS volume as well as a write of thedata to its local storage 330, 334, 338 resources. In some alternativeembodiments, data may only be written to the local storage 330, 334, 338resources within a particular cloud comping instance 340 a, 340 b, 340n. In an alternative embodiment, rather than using the block storage342, 344, 346 that is offered by the cloud computing environment 316 asNVRAM, actual RAM on each of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338 may be used as NVRAM, therebydecreasing network utilization costs that would be associated with usingan EBS volume as the NVRAM. In yet another embodiment, high performanceblock storage resources such as one or more Azure Ultra Disks may beutilized as the NVRAM.

The storage controller applications 324, 326 may be used to performvarious tasks such as deduplicating the data contained in the request,compressing the data contained in the request, determining where to thewrite the data contained in the request, and so on, before ultimatelysending a request to write a deduplicated, encrypted, or otherwisepossibly updated version of the data to one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334,338. Either cloud computing instance 320, 322, in some embodiments, mayreceive a request to read data from the cloud-based storage system 318and may ultimately send a request to read data to one or more of thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338.

When a request to write data is received by a particular cloud computinginstance 340 a, 340 b, 340 n with local storage 330, 334, 338, thesoftware daemon 328, 332, 336 may be configured to not only write thedata to its own local storage 330, 334, 338 resources and anyappropriate block storage 342, 344, 346 resources, but the softwaredaemon 328, 332, 336 may also be configured to write the data tocloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n. The cloud-based object storage348 that is attached to the particular cloud computing instance 340 a,340 b, 340 n may be embodied, for example, as Amazon Simple StorageService (‘S3’). In other embodiments, the cloud computing instances 320,322 that each include the storage controller application 324, 326 mayinitiate the storage of the data in the local storage 330, 334, 338 ofthe cloud computing instances 340a, 340 b, 340 n and the cloud-basedobject storage 348. In other embodiments, rather than using both thecloud computing instances 340 a, 340 b, 340 n with local storage 330,334, 338 (also referred to herein as ‘virtual drives’) and thecloud-based object storage 348 to store data, a persistent storage layermay be implemented in other ways. For example, one or more Azure Ultradisks may be used to persistently store data (e.g., after the data hasbeen written to the NVRAM layer).

While the local storage 330, 334, 338 resources and the block storage342, 344, 346 resources that are utilized by the cloud computinginstances 340 a, 340 b, 340 n may support block-level access, thecloud-based object storage 348 that is attached to the particular cloudcomputing instance 340 a, 340 b, 340 n supports only object-basedaccess. The software daemon 328, 332, 336 may therefore be configured totake blocks of data, package those blocks into objects, and write theobjects to the cloud-based object storage 348 that is attached to theparticular cloud computing instance 340 a, 340 b, 340 n.

Consider an example in which data is written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n in 1MB blocks. In such an example, assume that a user of the cloud-basedstorage system 318 issues a request to write data that, after beingcompressed and deduplicated by the storage controller application 324,326 results in the need to write 5 MB of data. In such an example,writing the data to the local storage 330, 334, 338 resources and theblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340 n is relatively straightforward as5 blocks that are 1 MB in size are written to the local storage 330,334, 338 resources and the block storage 342, 344, 346 resources thatare utilized by the cloud computing instances 340 a, 340 b, 340 n. Insuch an example, the software daemon 328, 332, 336 may also beconfigured to create five objects containing distinct 1 MB chunks of thedata. As such, in some embodiments, each object that is written to thecloud-based object storage 348 may be identical (or nearly identical) insize. Readers will appreciate that in such an example, metadata that isassociated with the data itself may be included in each object (e.g.,the first 1 MB of the object is data and the remaining portion ismetadata associated with the data). Readers will appreciate that thecloud-based object storage 348 may be incorporated into the cloud-basedstorage system 318 to increase the durability of the cloud-based storagesystem 318.

In some embodiments, all data that is stored by the cloud-based storagesystem 318 may be stored in both: 1) the cloud-based object storage 348,and 2) at least one of the local storage 330, 334, 338 resources orblock storage 342, 344, 346 resources that are utilized by the cloudcomputing instances 340 a, 340 b, 340n. In such embodiments, the localstorage 330, 334, 338 resources and block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340 n may effectively operate as cache that generally includes alldata that is also stored in S3, such that all reads of data may beserviced by the cloud computing instances 340 a, 340 b, 340 n withoutrequiring the cloud computing instances 340 a, 340 b, 340 n to accessthe cloud-based object storage 348. Readers will appreciate that inother embodiments, however, all data that is stored by the cloud-basedstorage system 318 may be stored in the cloud-based object storage 348,but less than all data that is stored by the cloud-based storage system318 may be stored in at least one of the local storage 330, 334, 338resources or block storage 342, 344, 346 resources that are utilized bythe cloud computing instances 340 a, 340 b, 340 n. In such an example,various policies may be utilized to determine which subset of the datathat is stored by the cloud-based storage system 318 should reside inboth: 1) the cloud-based object storage 348, and 2) at least one of thelocal storage 330, 334, 338 resources or block storage 342, 344, 346resources that are utilized by the cloud computing instances 340 a, 340b, 340n.

One or more modules of computer program instructions that are executingwithin the cloud-based storage system 318 (e.g., a monitoring modulethat is executing on its own EC2 instance) may be designed to handle thefailure of one or more of the cloud computing instances 340 a, 340 b,340 n with local storage 330, 334, 338. In such an example, themonitoring module may handle the failure of one or more of the cloudcomputing instances 340 a, 340 b, 340 n with local storage 330, 334, 338by creating one or more new cloud computing instances with localstorage, retrieving data that was stored on the failed cloud computinginstances 340 a, 340 b, 340 n from the cloud-based object storage 348,and storing the data retrieved from the cloud-based object storage 348in local storage on the newly created cloud computing instances. Readerswill appreciate that many variants of this process may be implemented.

Readers will appreciate that various performance aspects of thecloud-based storage system 318 may be monitored (e.g., by a monitoringmodule that is executing in an EC2 instance) such that the cloud-basedstorage system 318 can be scaled-up or scaled-out as needed. Forexample, if the cloud computing instances 320, 322 that are used tosupport the execution of a storage controller application 324, 326 areundersized and not sufficiently servicing the I/0 requests that areissued by users of the cloud-based storage system 318, a monitoringmodule may create a new, more powerful cloud computing instance (e.g., acloud computing instance of a type that includes more processing power,more memory, etc . . . ) that includes the storage controllerapplication such that the new, more powerful cloud computing instancecan begin operating as the primary controller. Likewise, if themonitoring module determines that the cloud computing instances 320, 322that are used to support the execution of a storage controllerapplication 324, 326 are oversized and that cost savings could be gainedby switching to a smaller, less powerful cloud computing instance, themonitoring module may create a new, less powerful (and less expensive)cloud computing instance that includes the storage controllerapplication such that the new, less powerful cloud computing instancecan begin operating as the primary controller.

The storage systems described above may carry out intelligent databackup techniques through which data stored in the storage system may becopied and stored in a distinct location to avoid data loss in the eventof equipment failure or some other form of catastrophe. For example, thestorage systems described above may be configured to examine each backupto avoid restoring the storage system to an undesirable state. Consideran example in which malware infects the storage system. In such anexample, the storage system may include software resources 314 that canscan each backup to identify backups that were captured before themalware infected the storage system and those backups that were capturedafter the malware infected the storage system. In such an example, thestorage system may restore itself from a backup that does not includethe malware—or at least not restore the portions of a backup thatcontained the malware. In such an example, the storage system mayinclude software resources 314 that can scan each backup to identify thepresences of malware (or a virus, or some other undesirable), forexample, by identifying write operations that were serviced by thestorage system and originated from a network subnet that is suspected tohave delivered the malware, by identifying write operations that wereserviced by the storage system and originated from a user that issuspected to have delivered the malware, by identifying write operationsthat were serviced by the storage system and examining the content ofthe write operation against fingerprints of the malware, and in manyother ways.

Readers will further appreciate that the backups (often in the form ofone or more snapshots) may also be utilized to perform rapid recovery ofthe storage system. Consider an example in which the storage system isinfected with ransomware that locks users out of the storage system. Insuch an example, software resources 314 within the storage system may beconfigured to detect the presence of ransomware and may be furtherconfigured to restore the storage system to a point-in-time, using theretained backups, prior to the point-in-time at which the ransomwareinfected the storage system. In such an example, the presence ofransomware may be explicitly detected through the use of software toolsutilized by the system, through the use of a key (e.g., a USB drive)that is inserted into the storage system, or in a similar way. Likewise,the presence of ransomware may be inferred in response to systemactivity meeting a predetermined fingerprint such as, for example, noreads or writes coming into the system for a predetermined period oftime.

Readers will appreciate that the various components described above maybe grouped into one or more optimized computing packages as convergedinfrastructures. Such converged infrastructures may include pools ofcomputers, storage and networking resources that can be shared bymultiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may beimplemented with a converged infrastructure reference architecture, withstandalone appliances, with a software driven hyper-converged approach(e.g., hyper-converged infrastructures), or in other ways.

Readers will appreciate that the storage systems described in thisdisclosure may be useful for supporting various types of softwareapplications. In fact, the storage systems may be ‘application aware’ inthe sense that the storage systems may obtain, maintain, or otherwisehave access to information describing connected applications (e.g.,applications that utilize the storage systems) to optimize the operationof the storage system based on intelligence about the applications andtheir utilization patterns. For example, the storage system may optimizedata layouts, optimize caching behaviors, optimize ‘QoS’ levels, orperform some other optimization that is designed to improve the storageperformance that is experienced by the application.

As an example of one type of application that may be supported by thestorage systems describe herein, the storage system 306 may be useful insupporting artificial intelligence (‘AI’) applications, databaseapplications, XOps projects (e.g., DevOps projects, DataOps projects,MLOps projects, ModelOps projects, PlatformOps projects), electronicdesign automation tools, event-driven software applications, highperformance computing applications, simulation applications, high-speeddata capture and analysis applications, machine learning applications,media production applications, media serving applications, picturearchiving and communication systems (‘PACS’) applications, softwaredevelopment applications, virtual reality applications, augmentedreality applications, and many other types of applications by providingstorage resources to such applications.

In view of the fact that the storage systems include compute resources,storage resources, and a wide variety of other resources, the storagesystems may be well suited to support applications that are resourceintensive such as, for example, AI applications. AI applications may bedeployed in a variety of fields, including: predictive maintenance inmanufacturing and related fields, healthcare applications such aspatient data & risk analytics, retail and marketing deployments (e.g.,search advertising, social media advertising), supply chains solutions,fintech solutions such as business analytics & reporting tools,operational deployments such as real-time analytics tools, applicationperformance management tools, IT infrastructure management tools, andmany others.

Such AI applications may enable devices to perceive their environmentand take actions that maximize their chance of success at some goal.Examples of such AI applications can include IBM Watson™, MicrosoftOxford™, Google DeepMind™, Baidu Minwa™, and others.

The storage systems described above may also be well suited to supportother types of applications that are resource intensive such as, forexample, machine learning applications. Machine learning applicationsmay perform various types of data analysis to automate analytical modelbuilding. Using algorithms that iteratively learn from data, machinelearning applications can enable computers to learn without beingexplicitly programmed. One particular area of machine learning isreferred to as reinforcement learning, which involves taking suitableactions to maximize reward in a particular situation.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. Such GPUs may includethousands of cores that are well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks, including the development ofmulti-layer neural networks, have ignited a new wave of algorithms andtools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslateTM which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

In order for the storage systems described above to serve as a data hubor as part of an AI deployment, in some embodiments the storage systemsmay be configured to provide DMA between storage devices that areincluded in the storage systems and one or more GPUs that are used in anAI or big data analytics pipeline. The one or more GPUs may be coupledto the storage system, for example, via NVMe-over-Fabrics (‘NVMe-oF’)such that bottlenecks such as the host CPU can be bypassed and thestorage system (or one of the components contained therein) can directlyaccess GPU memory. In such an example, the storage systems may leverageAPI hooks to the GPUs to transfer data directly to the GPUs. Forexample, the GPUs may be embodied as Nvidia™ GPUs and the storagesystems may support GPUDirect Storage (‘GDS’) software, or have similarproprietary software, that enables the storage system to transfer datato the GPUs via RDMA or similar mechanism.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. The storage systems described above mayalso be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage or use of (among other types of data)blockchains and derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Blockchains and the storage systems described herein may beleveraged to support on-chain storage of data as well as off-chainstorage of data.

Off-chain storage of data can be implemented in a variety of ways andcan occur when the data itself is not stored within the blockchain. Forexample, in one embodiment, a hash function may be utilized and the dataitself may be fed into the hash function to generate a hash value. Insuch an example, the hashes of large pieces of data may be embeddedwithin transactions, instead of the data itself. Readers will appreciatethat, in other embodiments, alternatives to blockchains may be used tofacilitate the decentralized storage of information. For example, onealternative to a blockchain that may be used is a blockweave. Whileconventional blockchains store every transaction to achieve validation,a blockweave permits secure decentralization without the usage of theentire chain, thereby enabling low cost on-chain storage of data. Suchblockweaves may utilize a consensus mechanism that is based on proof ofaccess (PoA) and proof of work (PoW).

The storage systems described above may, either alone or in combinationwith other computing devices, be used to support in-memory computingapplications. In-memory computing involves the storage of information inRAM that is distributed across a cluster of computers. Readers willappreciate that the storage systems described above, especially thosethat are configurable with customizable amounts of processing resources,storage resources, and memory resources (e.g., those systems in whichblades that contain configurable amounts of each type of resource), maybe configured in a way so as to provide an infrastructure that cansupport in-memory computing. Likewise, the storage systems describedabove may include component parts (e.g., NVDIMMs, 3D crosspoint storagethat provide fast random access memory that is persistent) that canactually provide for an improved in-memory computing environment ascompared to in-memory computing environments that rely on RAMdistributed across dedicated servers.

In some embodiments, the storage systems described above may beconfigured to operate as a hybrid in-memory computing environment thatincludes a universal interface to all storage media (e.g., RAM, flashstorage, 3D crosspoint storage). In such embodiments, users may have noknowledge regarding the details of where their data is stored but theycan still use the same full, unified API to address data. In suchembodiments, the storage system may (in the background) move data to thefastest layer available—including intelligently placing the data independence upon various characteristics of the data or in dependenceupon some other heuristic. In such an example, the storage systems mayeven make use of existing products such as Apache Ignite and GridGain tomove data between the various storage layers, or the storage systems maymake use of custom software to move data between the various storagelayers. The storage systems described herein may implement variousoptimizations to improve the performance of in-memory computing such as,for example, having computations occur as close to the data as possible.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. As an additional example,some IoT devices such as connected video cameras may not be well-suitedfor the utilization of cloud-based resources as it may be impractical(not only from a privacy perspective, security perspective, or afinancial perspective) to send the data to the cloud simply because ofthe pure volume of data that is involved. As such, many tasks thatreally on data processing, storage, or communications may be bettersuited by platforms that include edge solutions such as the storagesystems described above.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics, including being leveraged as part of a composable dataanalytics pipeline where containerized analytics architectures, forexample, make analytics capabilities more composable. Big data analyticsmay be generally described as the process of examining large and varieddata sets to uncover hidden patterns, unknown correlations, markettrends, customer preferences and other useful information that can helporganizations make more-informed business decisions. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa™, Apple Siri™, Google Voice™, Samsung Bixby,Microsoft Cortana™, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution of artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver a widerange of transparently immersive experiences (including those that usedigital twins of various “things” such as people, places, processes,systems, and so on) where technology can introduce transparency betweenpeople, businesses, and things. Such transparently immersive experiencesmay be delivered as augmented reality technologies, connected homes,virtual reality technologies, brain-computer interfaces, humanaugmentation technologies, nanotube electronics, volumetric displays, 4Dprinting technologies, or others.

The storage systems described above may also, either alone or incombination with other computing environments, be used to support a widevariety of digital platforms. Such digital platforms can include, forexample, 5G wireless systems and platforms, digital twin platforms, edgecomputing platforms, IoT platforms, quantum computing platforms,serverless PaaS, software-defined security, neuromorphic computingplatforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Similarly, as part of a suite oftools to secure data stored on the storage system, the storage systemsdescribed above may implement various encryption technologies andschemes, including lattice cryptography. Lattice cryptography caninvolve constructions of cryptographic primitives that involve lattices,either in the construction itself or in the security proof. Unlikepublic-key schemes such as the RSA, Diffie-Hellman or Elliptic-Curvecryptosystems, which are easily attacked by a quantum computer, somelattice-based constructions appear to be resistant to attack by bothclassical and quantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

The storage systems described above may also be paired withFPGA-accelerated servers as part of a larger AI or ML infrastructure.Such FPGA-accelerated servers may reside near (e.g., in the same datacenter) the storage systems described above or even incorporated into anappliance that includes one or more storage systems, one or moreFPGA-accelerated servers, networking infrastructure that supportscommunications between the one or more storage systems and the one ormore FPGA-accelerated servers, as well as other hardware and softwarecomponents. Alternatively, FPGA-accelerated servers may reside within acloud computing environment that may be used to perform compute-relatedtasks for AI and ML jobs. Any of the embodiments described above may beused to collectively serve as a FPGA-based AI or ML platform. Readerswill appreciate that, in some embodiments of the FPGA-based AI or MLplatform, the FPGAs that are contained within the FPGA-acceleratedservers may be reconfigured for different types of ML models (e.g.,LSTMs, CNNs, GRUs). The ability to reconfigure the FPGAs that arecontained within the FPGA-accelerated servers may enable theacceleration of a ML or AI application based on the most optimalnumerical precision and memory model being used. Readers will appreciatethat by treating the collection of FPGA-accelerated servers as a pool ofFPGAs, any CPU in the data center may utilize the pool of FPGAs as ashared hardware microservice, rather than limiting a server to dedicatedaccelerators plugged into it.

The FPGA-accelerated servers and the GPU-accelerated servers describedabove may implement a model of computing where, rather than keeping asmall amount of data in a CPU and running a long stream of instructionsover it as occurred in more traditional computing models, the machinelearning model and parameters are pinned into the high-bandwidth on-chipmemory with lots of data streaming though the high-bandwidth on-chipmemory. FPGAs may even be more efficient than GPUs for this computingmodel, as the FPGAs can be programmed with only the instructions neededto run this kind of computing model.

The storage systems described above may be configured to provideparallel storage, for example, through the use of a parallel file systemsuch as BeeGFS. Such parallel files systems may include a distributedmetadata architecture. For example, the parallel file system may includea plurality of metadata servers across which metadata is distributed, aswell as components that include services for clients and storageservers.

The systems described above can support the execution of a wide array ofsoftware applications. Such software applications can be deployed in avariety of ways, including container-based deployment models.Containerized applications may be managed using a variety of tools. Forexample, containerized applications may be managed using Docker Swarm,Kubernetes, and others. Containerized applications may be used tofacilitate a serverless, cloud native computing deployment andmanagement model for software applications. In support of a serverless,cloud native computing deployment and management model for softwareapplications, containers may be used as part of an event handlingmechanisms (e.g., AWS Lambdas) such that various events cause acontainerized application to be spun up to operate as an event handler.

The systems described above may be deployed in a variety of ways,including being deployed in ways that support fifth generation (‘5G’)networks. 5G networks may support substantially faster datacommunications than previous generations of mobile communicationsnetworks and, as a consequence may lead to the disaggregation of dataand computing resources as modern massive data centers may become lessprominent and may be replaced, for example, by more-local, micro datacenters that are close to the mobile-network towers. The systemsdescribed above may be included in such local, micro data centers andmay be part of or paired to multi-access edge computing (‘MEC’) systems.Such MEC systems may enable cloud computing capabilities and an ITservice environment at the edge of the cellular network. By runningapplications and performing related processing tasks closer to thecellular customer, network congestion may be reduced and applicationsmay perform better.

The storage systems described above may also be configured to implementNVMe Zoned Namespaces. Through the use of NVMe Zoned Namespaces, thelogical address space of a namespace is divided into zones. Each zoneprovides a logical block address range that must be written sequentiallyand explicitly reset before rewriting, thereby enabling the creation ofnamespaces that expose the natural boundaries of the device and offloadmanagement of internal mapping tables to the host. In order to implementNVMe Zoned Name Spaces (‘ZNS’), ZNS SSDs or some other form of zonedblock devices may be utilized that expose a namespace logical addressspace using zones. With the zones aligned to the internal physicalproperties of the device, several inefficiencies in the placement ofdata can be eliminated. In such embodiments, each zone may be mapped,for example, to a separate application such that functions like wearlevelling and garbage collection could be performed on a per-zone orper-application basis rather than across the entire device. In order tosupport ZNS, the storage controllers described herein may be configuredwith to interact with zoned block devices through the usage of, forexample, the Linux kernel zoned block device interface or other tools.

The storage systems described above may also be configured to implementzoned storage in other ways such as, for example, through the usage ofshingled magnetic recording (SMR) storage devices. In examples wherezoned storage is used, device-managed embodiments may be deployed wherethe storage devices hide this complexity by managing it in the firmware,presenting an interface like any other storage device. Alternatively,zoned storage may be implemented via a host-managed embodiment thatdepends on the operating system to know how to handle the drive, andonly write sequentially to certain regions of the drive. Zoned storagemay similarly be implemented using a host-aware embodiment in which acombination of a drive managed and host managed implementation isdeployed.

The storage systems described herein may be used to form a data lake. Adata lake may operate as the first place that an organization's dataflows to, where such data may be in a raw format. Metadata tagging maybe implemented to facilitate searches of data elements in the data lake,especially in embodiments where the data lake contains multiple storesof data, in formats not easily accessible or readable (e.g.,unstructured data, semi-structured data, structured data). From the datalake, data may go downstream to a data warehouse where data may bestored in a more processed, packaged, and consumable format. The storagesystems described above may also be used to implement such a datawarehouse. In addition, a data mart or data hub may allow for data thatis even more easily consumed, where the storage systems described abovemay also be used to provide the underlying storage resources necessaryfor a data mart or data hub. In embodiments, queries the data lake mayrequire a schema-on-read approach, where data is applied to a plan orschema as it is pulled out of a stored location, rather than as it goesinto the stored location.

The storage systems described herein may also be configured implement arecovery point objective (‘RPO’), which may be establish by a user,established by an administrator, established as a system default,established as part of a storage class or service that the storagesystem is participating in the delivery of, or in some other way. A“recovery point objective” is a goal for the maximum time differencebetween the last update to a source dataset and the last recoverablereplicated dataset update that would be correctly recoverable, given areason to do so, from a continuously or frequently updated copy of thesource dataset. An update is correctly recoverable if it properly takesinto account all updates that were processed on the source dataset priorto the last recoverable replicated dataset update.

In synchronous replication, the RPO would be zero, meaning that undernormal operation, all completed updates on the source dataset should bepresent and correctly recoverable on the copy dataset. In best effortnearly synchronous replication, the RPO can be as low as a few seconds.In snapshot-based replication, the RPO can be roughly calculated as theinterval between snapshots plus the time to transfer the modificationsbetween a previous already transferred snapshot and the most recentto-be-replicated snapshot.

If updates accumulate faster than they are replicated, then an RPO canbe missed. If more data to be replicated accumulates between twosnapshots, for snapshot-based replication, than can be replicatedbetween taking the snapshot and replicating that snapshot's cumulativeupdates to the copy, then the RPO can be missed. If, again insnapshot-based replication, data to be replicated accumulates at afaster rate than could be transferred in the time between subsequentsnapshots, then replication can start to fall further behind which canextend the miss between the expected recovery point objective and theactual recovery point that is represented by the last correctlyreplicated update.

The storage systems described above may also be part of a shared nothingstorage cluster. In a shared nothing storage cluster, each node of thecluster has local storage and communicates with other nodes in thecluster through networks, where the storage used by the cluster is (ingeneral) provided only by the storage connected to each individual node.A collection of nodes that are synchronously replicating a dataset maybe one example of a shared nothing storage cluster, as each storagesystem has local storage and communicates to other storage systemsthrough a network, where those storage systems do not (in general) usestorage from somewhere else that they share access to through some kindof interconnect. In contrast, some of the storage systems describedabove are themselves built as a shared-storage cluster, since there aredrive shelves that are shared by the paired controllers. Other storagesystems described above, however, are built as a shared nothing storagecluster, as all storage is local to a particular node (e.g., a blade)and all communication is through networks that link the compute nodestogether.

In other embodiments, other forms of a shared nothing storage clustercan include embodiments where any node in the cluster has a local copyof all storage they need, and where data is mirrored through asynchronous style of replication to other nodes in the cluster either toensure that the data isn't lost or because other nodes are also usingthat storage. In such an embodiment, if a new cluster node needs somedata, that data can be copied to the new node from other nodes that havecopies of the data.

In some embodiments, mirror-copy-based shared storage clusters may storemultiple copies of all the cluster's stored data, with each subset ofdata replicated to a particular set of nodes, and different subsets ofdata replicated to different sets of nodes. In some variations,embodiments may store all of the cluster's stored data in all nodes,whereas in other variations nodes may be divided up such that a firstset of nodes will all store the same set of data and a second, differentset of nodes will all store a different set of data.

Readers will appreciate that RAFT-based databases (e.g., etcd) mayoperate like shared-nothing storage clusters where all RAFT nodes storeall data. The amount of data stored in a RAFT cluster, however, may belimited so that extra copies don't consume too much storage. A containerserver cluster might also be able to replicate all data to all clusternodes, presuming the containers don't tend to be too large and theirbulk data (the data manipulated by the applications that run in thecontainers) is stored elsewhere such as in an S3 cluster or an externalfile server. In such an example, the container storage may be providedby the cluster directly through its shared-nothing storage model, withthose containers providing the images that form the executionenvironment for parts of an application or service.

For further explanation, FIG. 3D illustrates an exemplary computingdevice 350 that may be specifically configured to perform one or more ofthe processes described herein. As shown in FIG. 3D, computing device350 may include a communication interface 352, a processor 354, astorage device 356, and an input/output (“I/O”) module 358communicatively connected one to another via a communicationinfrastructure 360. While an exemplary computing device 350 is shown inFIG. 3D, the components illustrated in FIG. 3D are not intended to belimiting. Additional or alternative components may be used in otherembodiments. Components of computing device 350 shown in FIG. 3D willnow be described in additional detail.

Communication interface 352 may be configured to communicate with one ormore computing devices. Examples of communication interface 352 include,without limitation, a wired network interface (such as a networkinterface card), a wireless network interface (such as a wirelessnetwork interface card), a modem, an audio/video connection, and anyother suitable interface.

Processor 354 generally represents any type or form of processing unitcapable of processing data and/or interpreting, executing, and/ordirecting execution of one or more of the instructions, processes,and/or operations described herein. Processor 354 may perform operationsby executing computer-executable instructions 362 (e.g., an application,software, code, and/or other executable data instance) stored in storagedevice 356.

Storage device 356 may include one or more data storage media, devices,or configurations and may employ any type, form, and combination of datastorage media and/or device. For example, storage device 356 mayinclude, but is not limited to, any combination of the non-volatilemedia and/or volatile media described herein. Electronic data, includingdata described herein, may be temporarily and/or permanently stored instorage device 356. For example, data representative ofcomputer-executable instructions 362 configured to direct processor 354to perform any of the operations described herein may be stored withinstorage device 356. In some examples, data may be arranged in one ormore databases residing within storage device 356.

I/O module 358 may include one or more I/O modules configured to receiveuser input and provide user output. I/O module 358 may include anyhardware, firmware, software, or combination thereof supportive of inputand output capabilities. For example, I/O module 358 may includehardware and/or software for capturing user input, including, but notlimited to, a keyboard or keypad, a touchscreen component (e.g.,touchscreen display), a receiver (e.g., an RF or infrared receiver),motion sensors, and/or one or more input buttons.

I/O module 358 may include one or more devices for presenting output toa user, including, but not limited to, a graphics engine, a display(e.g., a display screen), one or more output drivers (e.g., displaydrivers), one or more audio speakers, and one or more audio drivers. Incertain embodiments, I/O module 358 is configured to provide graphicaldata to a display for presentation to a user. The graphical data may berepresentative of one or more graphical user interfaces and/or any othergraphical content as may serve a particular implementation. In someexamples, any of the systems, computing devices, and/or other componentsdescribed herein may be implemented by computing device 350.

For further explanation, FIG. 3E illustrates an example of a fleet ofstorage systems 376 for providing storage services (also referred toherein as ‘data services’). The fleet of storage systems 376 depicted inFIG. 3 includes a plurality of storage systems 374 a, 374 b, 374 c, 374d, 374 n that may each be similar to the storage systems describedherein. The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in thefleet of storage systems 376 may be embodied as identical storagesystems or as different types of storage systems. For example, two ofthe storage systems 374 a, 374 n depicted in FIG. 3E are depicted asbeing cloud-based storage systems, as the resources that collectivelyform each of the storage systems 374 a, 374 n are provided by distinctcloud services providers 370, 372. For example, the first cloud servicesprovider 370 may be Amazon AWS™ whereas the second cloud servicesprovider 372 is Microsoft Azure, although in other embodiments one ormore public clouds, private clouds, or combinations thereof may be usedto provide the underlying resources that are used to form a particularstorage system in the fleet of storage systems 376.

The example depicted in FIG. 3E includes an edge management service 382for delivering storage services in accordance with some embodiments ofthe present disclosure. The storage services (also referred to herein as‘data services’) that are delivered may include, for example, servicesto provide a certain amount of storage to a consumer, services toprovide storage to a consumer in accordance with a predetermined servicelevel agreement, services to provide storage to a consumer in accordancewith predetermined regulatory requirements, and many others.

The edge management service 382 depicted in FIG. 3E may be embodied, forexample, as one or more modules of computer program instructionsexecuting on computer hardware such as one or more computer processors.Alternatively, the edge management service 382 may be embodied as one ormore modules of computer program instructions executing on a virtualizedexecution environment such as one or more virtual machines, in one ormore containers, or in some other way. In other embodiments, the edgemanagement service 382 may be embodied as a combination of theembodiments described above, including embodiments where the one or moremodules of computer program instructions that are included in the edgemanagement service 382 are distributed across multiple physical orvirtual execution environments.

The edge management service 382 may operate as a gateway for providingstorage services to storage consumers, where the storage servicesleverage storage offered by one or more storage systems 374 a, 374 b,374 c, 374 d, 374 n. For example, the edge management service 382 may beconfigured to provide storage services to host devices 378 a, 378 b, 378c, 378 d, 378 n that are executing one or more applications that consumethe storage services. In such an example, the edge management service382 may operate as a gateway between the host devices 378 a, 378 b, 378c, 378 d, 378 n and the storage systems 374 a, 374 b, 374 c, 374 d, 374n, rather than requiring that the host devices 378 a, 378 b, 378 c, 378d, 378 n directly access the storage systems 374 a, 374 b, 374 c, 374 d,374 n.

The edge management service 382 of FIG. 3E exposes a storage servicesmodule 380 to the host devices 378 a, 378 b, 378 c, 378 d, 378 n of FIG.3E, although in other embodiments the edge management service 382 mayexpose the storage services module 380 to other consumers of the variousstorage services. The various storage services may be presented toconsumers via one or more user interfaces, via one or more APIs, orthrough some other mechanism provided by the storage services module380. As such, the storage services module 380 depicted in FIG. 3E may beembodied as one or more modules of computer program instructionsexecuting on physical hardware, on a virtualized execution environment,or combinations thereof, where executing such modules causes enables aconsumer of storage services to be offered, select, and access thevarious storage services.

The edge management service 382 of FIG. 3E also includes a systemmanagement services module 384. The system management services module384 of FIG. 3E includes one or more modules of computer programinstructions that, when executed, perform various operations incoordination with the storage systems 374 a, 374 b, 374 c, 374 d, 374 nto provide storage services to the host devices 378 a, 378 b, 378 c, 378d, 378 n. The system management services module 384 may be configured,for example, to perform tasks such as provisioning storage resourcesfrom the storage systems 374 a, 374 b, 374 c, 374 d, 374 n via one ormore APIs exposed by the storage systems 374 a, 374 b, 374 c, 374 d, 374n, migrating datasets or workloads amongst the storage systems 374 a,374 b, 374 c, 374 d, 374 n via one or more APIs exposed by the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n, setting one or more tunableparameters (i.e., one or more configurable settings) on the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n via one or more APIs exposedby the storage systems 374 a, 374 b, 374 c, 374 d, 374 n, and so on. Forexample, many of the services described below relate to embodimentswhere the storage systems 374 a, 374 b, 374 c, 374 d, 374 n areconfigured to operate in some way. In such examples, the systemmanagement services module 384 may be responsible for using APIs (orsome other mechanism) provided by the storage systems 374 a, 374 b, 374c, 374 d, 374 n to configure the storage systems 374 a, 374 b, 374 c,374 d, 374 n to operate in the ways described below.

In addition to configuring the storage systems 374 a, 374 b, 374 c, 374d, 374 n, the edge management service 382 itself may be configured toperform various tasks required to provide the various storage services.Consider an example in which the storage service includes a servicethat, when selected and applied, causes personally identifiableinformation (PIP) contained in a dataset to be obfuscated when thedataset is accessed. In such an example, the storage systems 374 a, 374b, 374 c, 374 d, 374 n may be configured to obfuscate PII when servicingread requests directed to the dataset. Alternatively, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may service reads by returningdata that includes the PII, but the edge management service 382 itselfmay obfuscate the PII as the data is passed through the edge managementservice 382 on its way from the storage systems 374 a, 374 b, 374 c, 374d, 374 n to the host devices 378 a, 378 b, 378 c, 378 d, 378 n.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may be embodied as one or more of the storage systems described abovewith reference to FIGS. 1A-3D, including variations thereof. In fact,the storage systems 374 a, 374 b, 374 c, 374 d, 374 n may serve as apool of storage resources where the individual components in that poolhave different performance characteristics, different storagecharacteristics, and so on. For example, one of the storage systems 374amay be a cloud-based storage system, another storage system 374 b may bea storage system that provides block storage, another storage system 374c may be a storage system that provides file storage, another storagesystem 374d may be a relatively high-performance storage system whileanother storage system 374 n may be a relatively low-performance storagesystem, and so on. In alternative embodiments, only a single storagesystem may be present.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n depicted in FIG.3E may also be organized into different failure domains so that thefailure of one storage system 374 a should be totally unrelated to thefailure of another storage system 374 b. For example, each of thestorage systems may receive power from independent power systems, eachof the storage systems may be coupled for data communications overindependent data communications networks, and so on. Furthermore, thestorage systems in a first failure domain may be accessed via a firstgateway whereas storage systems in a second failure domain may beaccessed via a second gateway. For example, the first gateway may be afirst instance of the edge management service 382 and the second gatewaymay be a second instance of the edge management service 382, includingembodiments where each instance is distinct, or each instance is part ofa distributed edge management service 382.

As an illustrative example of available storage services, storageservices may be presented to a user that are associated with differentlevels of data protection. For example, storage services may bepresented to the user that, when selected and enforced, guarantee theuser that data associated with that user will be protected such thatvarious recovery point objectives (‘RPO’) can be guaranteed. A firstavailable storage service may ensure, for example, that some datasetassociated with the user will be protected such that any data that ismore than 5 seconds old can be recovered in the event of a failure ofthe primary data store whereas a second available storage service mayensure that the dataset that is associated with the user will beprotected such that any data that is more than 5 minutes old can berecovered in the event of a failure of the primary data store.

An additional example of storage services that may be presented to auser, selected by a user, and ultimately applied to a dataset associatedwith the user can include one or more data compliance services. Suchdata compliance services may be embodied, for example, as services thatmay be provided to consumers (i.e., a user) the data compliance servicesto ensure that the user's datasets are managed in a way to adhere tovarious regulatory requirements. For example, one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to the General DataProtection Regulation (‘GDPR’), one or data compliance services may beoffered to a user to ensure that the user's datasets are managed in away so as to adhere to the Sarbanes-Oxley Act of 2002 (‘SOX’), or one ormore data compliance services may be offered to a user to ensure thatthe user's datasets are managed in a way so as to adhere to some otherregulatory act. In addition, the one or more data compliance servicesmay be offered to a user to ensure that the user's datasets are managedin a way so as to adhere to some non-governmental guidance (e.g., toadhere to best practices for auditing purposes), the one or more datacompliance services may be offered to a user to ensure that the user'sdatasets are managed in a way so as to adhere to a particular clients ororganizations requirements, and so on.

Consider an example in which a particular data compliance service isdesigned to ensure that a user's datasets are managed in a way so as toadhere to the requirements set forth in the GDPR. While a listing of allrequirements of the GDPR can be found in the regulation itself, for thepurposes of illustration, an example requirement set forth in the GDPRrequires that pseudonymization processes must be applied to stored datain order to transform personal data in such a way that the resultingdata cannot be attributed to a specific data subject without the use ofadditional information. For example, data encryption techniques can beapplied to render the original data unintelligible, and such dataencryption techniques cannot be reversed without access to the correctdecryption key. As such, the GDPR may require that the decryption key bekept separately from the pseudonymised data. One particular datacompliance service may be offered to ensure adherence to therequirements set forth in this paragraph.

In order to provide this particular data compliance service, the datacompliance service may be presented to a user (e.g., via a GUI) andselected by the user. In response to receiving the selection of theparticular data compliance service, one or more storage servicespolicies may be applied to a dataset associated with the user to carryout the particular data compliance service. For example, a storageservices policy may be applied requiring that the dataset be encryptedprior to be stored in a storage system, prior to being stored in a cloudenvironment, or prior to being stored elsewhere. In order to enforcethis policy, a requirement may be enforced not only requiring that thedataset be encrypted when stored, but a requirement may be put in placerequiring that the dataset be encrypted prior to transmitting thedataset (e.g., sending the dataset to another party). In such anexample, a storage services policy may also be put in place requiringthat any encryption keys used to encrypt the dataset are not stored onthe same system that stores the dataset itself. Readers will appreciatethat many other forms of data compliance services may be offered andimplemented in accordance with embodiments of the present disclosure.

The storage systems 374 a, 374 b, 374 c, 374 d, 374 n in the fleet ofstorage systems 376 may be managed collectively, for example, by one ormore fleet management modules. The fleet management modules may be partof or separate from the system management services module 384 depictedin FIG. 3E. The fleet management modules may perform tasks such asmonitoring the health of each storage system in the fleet, initiatingupdates or upgrades on one or more storage systems in the fleet,migrating workloads for loading balancing or other performance purposes,and many other tasks. As such, and for many other reasons, the storagesystems 374 a, 374 b, 374 c, 374 d, 374 n may be coupled to each othervia one or more data communications links in order to exchange databetween the storage systems 374 a, 374 b, 374 c, 374 d, 374 n.

The storage systems described herein may support various forms of datareplication. For example, two or more of the storage systems maysynchronously replicate a dataset between each other. In synchronousreplication, distinct copies of a particular dataset may be maintainedby multiple storage systems, but all accesses (e.g., a read) of thedataset should yield consistent results regardless of which storagesystem the access was directed to. For example, a read directed to anyof the storage systems that are synchronously replicating the datasetshould return identical results. As such, while updates to the versionof the dataset need not occur at exactly the same time, precautions mustbe taken to ensure consistent accesses to the dataset. For example, ifan update (e.g., a write) that is directed to the dataset is received bya first storage system, the update may only be acknowledged as beingcompleted if all storage systems that are synchronously replicating thedataset have applied the update to their copies of the dataset. In suchan example, synchronous replication may be carried out through the useof I/O forwarding (e.g., a write received at a first storage system isforwarded to a second storage system), communications between thestorage systems (e.g., each storage system indicating that it hascompleted the update), or in other ways.

In other embodiments, a dataset may be replicated through the use ofcheckpoints. In checkpoint-based replication (also referred to as‘nearly synchronous replication’), a set of updates to a dataset (e.g.,one or more write operations directed to the dataset) may occur betweendifferent checkpoints, such that a dataset has been updated to aspecific checkpoint only if all updates to the dataset prior to thespecific checkpoint have been completed. Consider an example in which afirst storage system stores a live copy of a dataset that is beingaccessed by users of the dataset. In this example, assume that thedataset is being replicated from the first storage system to a secondstorage system using checkpoint-based replication. For example, thefirst storage system may send a first checkpoint (at time t=0) to thesecond storage system, followed by a first set of updates to thedataset, followed by a second checkpoint (at time t=1), followed by asecond set of updates to the dataset, followed by a third checkpoint (attime t=2). In such an example, if the second storage system hasperformed all updates in the first set of updates but has not yetperformed all updates in the second set of updates, the copy of thedataset that is stored on the second storage system may be up-to-dateuntil the second checkpoint. Alternatively, if the second storage systemhas performed all updates in both the first set of updates and thesecond set of updates, the copy of the dataset that is stored on thesecond storage system may be up-to-date until the third checkpoint.Readers will appreciate that various types of checkpoints may be used(e.g., metadata only checkpoints), checkpoints may be spread out basedon a variety of factors (e.g., time, number of operations, an RPOsetting), and so on.

In other embodiments, a dataset may be replicated through snapshot-basedreplication (also referred to as ‘asynchronous replication’). Insnapshot-based replication, snapshots of a dataset may be sent from areplication source such as a first storage system to a replicationtarget such as a second storage system. In such an embodiment, eachsnapshot may include the entire dataset or a subset of the dataset suchas, for example, only the portions of the dataset that have changedsince the last snapshot was sent from the replication source to thereplication target. Readers will appreciate that snapshots may be senton-demand, based on a policy that takes a variety of factors intoconsideration (e.g., time, number of operations, an RPO setting), or insome other way.

The storage systems described above may, either alone or in combination,by configured to serve as a continuous data protection store. Acontinuous data protection store is a feature of a storage system thatrecords updates to a dataset in such a way that consistent images ofprior contents of the dataset can be accessed with a low timegranularity (often on the order of seconds, or even less), andstretching back for a reasonable period of time (often hours or days).These allow access to very recent consistent points in time for thedataset, and also allow access to access to points in time for a datasetthat might have just preceded some event that, for example, caused partsof the dataset to be corrupted or otherwise lost, while retaining closeto the maximum number of updates that preceded that event. Conceptually,they are like a sequence of snapshots of a dataset taken very frequentlyand kept for a long period of time, though continuous data protectionstores are often implemented quite differently from snapshots. A storagesystem implementing a data continuous data protection store may furtherprovide a means of accessing these points in time, accessing one or moreof these points in time as snapshots or as cloned copies, or revertingthe dataset back to one of those recorded points in time.

Over time, to reduce overhead, some points in the time held in acontinuous data protection store can be merged with other nearby pointsin time, essentially deleting some of these points in time from thestore. This can reduce the capacity needed to store updates. It may alsobe possible to convert a limited number of these points in time intolonger duration snapshots. For example, such a store might keep a lowgranularity sequence of points in time stretching back a few hours fromthe present, with some points in time merged or deleted to reduceoverhead for up to an additional day. Stretching back in the pastfurther than that, some of these points in time could be converted tosnapshots representing consistent point-in-time images from only everyfew hours.

Although some embodiments are described largely in the context of astorage system, readers of skill in the art will recognize thatembodiments of the present disclosure may also take the form of acomputer program product disposed upon computer readable storage mediafor use with any suitable processing system. Such computer readablestorage media may be any storage medium for machine-readableinformation, including magnetic media, optical media, solid-state media,or other suitable media. Examples of such media include magnetic disksin hard drives or diskettes, compact disks for optical drives, magnetictape, and others as will occur to those of skill in the art. Personsskilled in the art will immediately recognize that any computer systemhaving suitable programming means will be capable of executing the stepsdescribed herein as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theembodiments described in this specification are oriented to softwareinstalled and executing on computer hardware, nevertheless, alternativeembodiments implemented as firmware or as hardware are well within thescope of the present disclosure.

In some examples, a non-transitory computer-readable medium storingcomputer-readable instructions may be provided in accordance with theprinciples described herein. The instructions, when executed by aprocessor of a computing device, may direct the processor and/orcomputing device to perform one or more operations, including one ormore of the operations described herein. Such instructions may be storedand/or transmitted using any of a variety of known computer-readablemedia.

A non-transitory computer-readable medium as referred to herein mayinclude any non-transitory storage medium that participates in providingdata (e.g., instructions) that may be read and/or executed by acomputing device (e.g., by a processor of a computing device). Forexample, a non-transitory computer-readable medium may include, but isnot limited to, any combination of non-volatile storage media and/orvolatile storage media. Exemplary non-volatile storage media include,but are not limited to, read-only memory, flash memory, a solid-statedrive, a magnetic storage device (e.g., a hard disk, a floppy disk,magnetic tape, etc.), ferroelectric random-access memory (“RAM”), and anoptical disc (e.g., a compact disc, a digital video disc, a Blu-raydisc, etc.). Exemplary volatile storage media include, but are notlimited to, RAM (e.g., dynamic RAM).

Advantages and features of the present disclosure can be furtherdescribed by the following statements:

1. A method comprising: identifying, by a recovery process, a set ofimmutable layers of container images included in a dataset used by acontainer system to run containerized applications on a first cluster;copying, by the recovery process, the set of immutable layers ofcontainer images to a second cluster; receiving, by the recovery processafter the set of immutable layers of container images are copied to thesecond cluster, a recovery request to recover the containerizedapplications; and copying, by the recovery process in response to therecovery request, a set of mutable layers included in the dataset to thesecond cluster, the second cluster configured to use the copied set ofimmutable layers and the copied set of mutable layers to recover thecontainerized applications on the second cluster.

2. The method of any of the preceding statements, wherein the set ofimmutable layers of the container images comprises container imagelayers accessed and used by one or more compute nodes of the firstcluster to run the containerized applications on the first cluster.

3. The method of any of the preceding statements, wherein the set ofmutable layers are provided by a storage system as persistent storagefor the containerized applications running on the first cluster.

4. The method of any of the preceding statements, wherein copying theset of immutable layers included in the dataset to the second clustercomprises prioritizing more frequently used immutable layers over lessfrequently used immutable layers.

5. The method of any of the preceding statements, wherein copying theset of mutable layers included in the dataset to the second clustercomprises prioritizing more frequently used mutable layers over lessfrequently used mutable layers.

6. The method of any of the preceding statements, wherein copying theset of mutable layers included in the dataset to the second cluster inresponse to the recovery request comprises identifying and copying onlymutable layers in the set of mutable layers that have changed since theset of mutable layers was last copied to the second cluster.

7. The method of any of the preceding statements, wherein the set ofimmutable layers comprises only unique immutable layers in the dataset.

8. The method of any of the preceding statements, wherein the dataset ismanaged by a container-aware storage system that provides the immutablelayers and the mutable layers for use by the container system to run thecontainerized applications on the first cluster.

9. The method of any of the preceding statements, wherein thecontainer-aware storage system manages and provides the immutable layersand the mutable layers as volumes.

10. The method of any of the preceding statements, wherein thecontainer-aware storage system comprises containerized instances of thecontainer-aware storage system running on compute nodes of the firstcluster.

11. The method of any of the preceding statements, wherein the recoveryprocess is included in the container-aware storage system.

12. The method of any of the preceding statements, further comprising:detecting an updated immutable layer in the set of immutable layersincluded in the dataset; and copying the updated immutable layer to thesecond cluster.

13. The method of any of the preceding statements, further comprising:detecting a new immutable layer in the set of immutable layers includedin the dataset; and copying the new immutable layer to the secondcluster.

14. The method of any of the preceding statements, further comprising:receiving a recovery request to recover the containerized applicationsback to the first cluster; and identifying and copying, from the secondcluster to the first cluster, any of the mutable layers that havechanged on the second cluster.

15. A system comprising: one or more memories storingcomputer-executable instructions; and one or more processors to executethe computer-executable instructions to: identify a set of immutablelayers of container images included in a dataset used by a containersystem to run containerized applications on a first cluster; copy theset of immutable layers of container images to a second cluster;receive, after the set of immutable layers of container images arecopied to the second cluster, a recovery request to recover thecontainerized applications; and copy, in response to the recoveryrequest, a set of mutable layers included in the dataset to the secondcluster, the second cluster configured to use the copied set ofimmutable layers and the copied set of mutable layers to recover thecontainerized applications on the second cluster.

16. The system of any of the preceding statements, wherein: the set ofimmutable layers of the container images comprises container imagelayers accessed and used by one or more compute nodes of the firstcluster to run the containerized applications on the first cluster; andthe set of mutable layers are provided by a storage system as persistentstorage for the containerized applications running on the first cluster.

17. The system of any of the preceding statements, wherein the datasetis managed by a container-aware storage system that provides theimmutable layers and the mutable layers for use by the container systemto run the containerized applications on the first cluster.

18. The system of any of the preceding statements, wherein thecontainer-aware storage system manages and provides the immutable layersand the mutable layers as volumes.

19. The system of any of the preceding statements, wherein the one ormore processors are further configured to execute thecomputer-executable instructions to: receive a recovery request torecover the containerized applications back to the first cluster; andidentify and copy, from the second cluster to the first cluster, any ofthe mutable layers that have changed on the second cluster.

20. A non-transitory, computer-readable medium storing computerinstructions that, when executed, direct one or more processors of oneor more computing devices to: identify a set of immutable layers ofcontainer images included in a dataset used by a container system to runcontainerized applications on a first cluster; copy the set of immutablelayers of container images to a second cluster; receive, after the setof immutable layers of container images are copied to the secondcluster, a recovery request to recover the containerized applications;and copy, in response to the recovery request, a set of mutable layersincluded in the dataset to the second cluster, the second clusterconfigured to use the copied set of immutable layers and the copied setof mutable layers to recover the containerized applications on thesecond cluster.

One or more embodiments may be described herein with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

While particular combinations of various functions and features of theone or more embodiments are expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

In some embodiments, one or more storage systems or one or more elementsof storage systems (e.g., features, services, operations, components,etc. of storage systems), such as any of the illustrative storagesystems or storage system elements described herein, may be implementedin and/or provide storage services to one or more container systems. Acontainer system may include any system that supports execution of oneor more containerized applications or services. Such a service may besoftware deployed as infrastructure for building applications, foroperating a run-time environment, and/or as infrastructure for otherservices. In the discussion that follows, descriptions of containerizedapplications generally apply to containerized services as well.

A container may combine one or more elements of a containerized softwareapplication together with a runtime environment for operating thoseelements of the software application bundled into a single image. Forexample, each such container of a containerized application may includeexecutable code of the software application and various dependencies,libraries, and/or other components, together with network configurationsand configured access to additional resources, used by the elements ofthe software application within the particular container in order toenable operation of those elements. A containerized application can berepresented as a collection of such containers that together representall the elements of the application combined with the various run-timeenvironments needed for all those elements to run. As a result, thecontainerized application may be abstracted away from host operatingsystems as a combined collection of lightweight and portable packagesand configurations, where the containerized application may be uniformlydeployed and consistently executed in different computing environmentsthat use different container-compatible operating systems or differentinfrastructures. In some embodiments, a containerized application sharesa kernel with a host computer system and executes as an isolatedenvironment (an isolated collection of files and directories, processes,system and network resources, and configured access to additionalresources and capabilities) that is isolated by an operating system of ahost system in conjunction with a container management framework. Whenexecuted, a containerized application may provide one or morecontainerized workloads and/or services.

The container system may include and/or utilize a cluster of nodes. Forexample, the container system may be configured to manage deployment andexecution of containerized applications on one or more nodes in acluster. The containerized applications may utilize resources of thenodes, such as memory, processing and/or storage resources providedand/or accessed by the nodes. The storage resources may include any ofthe illustrative storage resources described herein and may includeon-node resources such as a local tree of files and directories,off-node resources such as external networked file systems, databases orobject stores, or both on-node and off-node resources. Access toadditional resources and capabilities that could be configured forcontainers of a containerized application could include specializedcomputation capabilities such as GPUs and AI/ML engines, or specializedhardware such as sensors and cameras.

In some embodiments, the container system may include a containerorchestration system (which may also be referred to as a containerorchestrator, a container orchestration platform, etc.) designed to makeit reasonably simple and for many use cases automated to deploy, scale,and manage containerized applications. In some embodiments, thecontainer system may include and/or communicate with a storagemanagement system configured to provision and manage storage resources(e.g., virtual volumes) for private or shared use by cluster nodesand/or containers of containerized applications.

FIG. 4 illustrates an example container system 400. In this example, thecontainer system 400 includes a container storage system 402 that may beconfigured to perform one or more storage management operations toorganize, provision, and manage storage resources for use by one or morecontainerized applications 404-1 through 404-L of container system 400.In particular, the container storage system 402 may organize storageresources into one or more storage pools 406 of storage resources foruse by containerized applications 404-1 through 404-L. The containerstorage system may itself be implemented as a containerized service.

The container system 400 may include or be implemented by one or morecontainer orchestration systems, including Kubernetes™, Mesos™, DockerSwarm™, among others. The container orchestration system may manage thecontainer system 400 running on a cluster 408 through servicesimplemented by a control node, depicted as 410, and may further managethe container storage system or the relationship between individualcontainers and their storage, memory and CPU limits, networking, andtheir access to additional resources or services.

A control plane of the container system 400 may implement services thatinclude: deploying applications via a controller 412, monitoringapplications via the controller 412, providing an interface via an APIserver 414, and scheduling deployments via scheduler 416. In thisexample, controller 412, scheduler 416, API server 414, and containerstorage system 402 are implemented on a single node, node 410. In otherexamples, for resiliency, the control plane may be implemented bymultiple, redundant nodes, where if a node that is providing managementservices for the container system 400 fails, then another, redundantnode may provide management services for the cluster 408.

A data plane of the container system 400 may include a set of nodes thatprovides container runtimes for executing containerized applications. Anindividual node within the cluster 408 may execute a container runtime,such as Docker™, and execute a container manager, or node agent, such asa kubelet in Kubernetes (not depicted) that communicates with thecontrol plane via a local network-connected agent (sometimes called aproxy), such as an agent 418. The agent 418 may route network traffic toand from containers using, for example, Internet Protocol (IP) portnumbers. For example, a containerized application may request a storageclass from the control plane, where the request is handled by thecontainer manager, and the container manager communicates the request tothe control plane using the agent 418.

Cluster 408 may include a set of nodes that run containers for managedcontainerized applications. A node may be a virtual or physical machine.A node may be a host system.

The container storage system 402 may orchestrate storage resources toprovide storage to the container system 400. For example, the containerstorage system 402 may provide persistent storage to containerizedapplications 404-1-404-L using the storage pool 406. The containerstorage system 402 may itself be deployed as a containerized applicationby a container orchestration system.

For example, the container storage system 402 application may bedeployed within cluster 408 and perform management functions forproviding storage to the containerized applications 404. Managementfunctions may include determining one or more storage pools fromavailable storage resources, provisioning virtual volumes on one or morenodes, replicating data, responding to and recovering from host andnetwork faults, or handling storage operations. The storage pool 406 mayinclude storage resources from one or more local or remote sources,where the storage resources may be different types of storage,including, as examples, block storage, file storage, and object storage.

The container storage system 402 may also be deployed on a set of nodesfor which persistent storage may be provided by the containerorchestration system. In some examples, the container storage system 402may be deployed on all nodes in a cluster 408 using, for example, aKubernetes DaemonSet. In this example, nodes 420-1 through 420-N providea container runtime where container storage system 402 executes. Inother examples, some, but not all nodes in a cluster may execute thecontainer storage system 402.

The container storage system 402 may handle storage on a node andcommunicate with the control plane of container system 400, to providedynamic volumes, including persistent volumes. A persistent volume maybe mounted on a node as a virtual volume, such as virtual volumes 422-1and 422-P. After a virtual volume 422 is mounted, containerizedapplications may request and use, or be otherwise configured to use,storage provided by the virtual volume 422. In this example, thecontainer storage system 402 may install a driver on a kernel of a node,where the driver handles storage operations directed to the virtualvolume. In this example, the driver may receive a storage operationdirected to a virtual volume, and in response, the driver may performthe storage operation on one or more storage resources within thestorage pool 406, possibly under direction from or using additionallogic within containers that implement the container storage system 402as a containerized service.

The container storage system 402 may, in response to being deployed as acontainerized service, determine available storage resources. Forexample, storage resources 424-1 through 424-M may include localstorage, remote storage (storage on a separate node in a cluster), orboth local and remote storage. Storage resources may also includestorage from external sources such as various combinations of blockstorage systems, file storage systems, and object storage systems. Thestorage resources 424-1 through 424-M may include any type(s) and/orconfiguration(s) of storage resources (e.g., any of the illustrativestorage resources described above), and the container storage system 402may be configured to determine the available storage resources in anysuitable way, including based on a configuration file. For example, aconfiguration file may specify account and authentication informationfor cloud-based object storage 348 or for a cloud-based storage system318. The container storage system 402 may also determine availability ofone or more storage devices 356 or one or more storage systems. Anaggregate amount of storage from one or more of storage device(s) 356,storage system(s), cloud-based storage system(s) 318, edge managementservices 382, cloud-based object storage 348, or any other storageresources, or any combination or sub-combination of such storageresources may be used to provide the storage pool 406. The storage pool406 is used to provision storage for the one or more virtual volumesmounted on one or more of the nodes 420 within cluster 408.

In some implementations, the container storage system 402 may createmultiple storage pools. For example, the container storage system 402may aggregate storage resources of a same type into an individualstorage pool. In this example, a storage type may be one of: a storagedevice 356, a storage array 102, a cloud-based storage system 318,storage via an edge management service 382, or a cloud-based objectstorage 348. Or it could be storage configured with a certain level ortype of redundancy or distribution, such as a particular combination ofstriping, mirroring, or erasure coding.

The container storage system 402 may execute within the cluster 408 as acontainerized container storage system service, where instances ofcontainers that implement elements of the containerized containerstorage system service may operate on different nodes within the cluster408. In this example, the containerized container storage system servicemay operate in conjunction with the container orchestration system ofthe container system 400 to handle storage operations, mount virtualvolumes to provide storage to a node, aggregate available storage into astorage pool 406, provision storage for a virtual volume from a storagepool 406, generate backup data, replicate data between nodes, clusters,environments, among other storage system operations. In some examples,the containerized container storage system service may provide storageservices across multiple clusters operating in distinct computingenvironments. For example, other storage system operations may includestorage system operations described above with respect to FIGS. 1-3 .Persistent storage provided by the containerized container storagesystem service may be used to implement stateful and/or resilientcontainerized applications.

The container storage system 402 may be configured to perform anysuitable storage operations of a storage system. For example, thecontainer storage system 402 may be configured to perform one or more ofthe illustrative storage management operations described herein tomanage storage resources used by the container system.

In some embodiments, one or more storage operations, including one ormore of the illustrative storage management operations described herein,may be containerized. For example, one or more storage operations may beimplemented as one or more containerized applications configured to beexecuted to perform the storage operation(s). Such containerized storageoperations may be executed in any suitable runtime environment to manageany storage system(s), including any of the illustrative storage systemsdescribed herein.

In some embodiments, a storage system, such as the container storagesystem 402 described above, may be configured to store, manage, andprovide immutable container images (e.g., an image of a containerizedapplication) to a container system for use by the container system torun container instances of the container images (e.g., a containerinstance of the containerized application). The storage system may befurther configured to provide storage services, such as persistent datastorage, to the container system (e.g., to container instances runningin the container system) and may leverage elements of the storageservices to store, manage, and provide container images to the containersystem. Such a storage system may be referred to as a container-awarestorage system.

In some embodiments, the container-aware storage system may use volumesto store, manage, and provide immutable container images to thecontainer system for use by the container system to run containerinstances of the container images. For example, the storage system mayreceive an immutable container image and store the immutable containerimage as a volume. This may include the storage system translating theimmutable container image into the volume, examples of which aredescribed herein, and storing the volume to one or more storageresources. The storage system may subsequently detect a request from acontainer system, such as a request to run a container instance of theimmutable container image in the container system and, in response tothe request, provide the volume to the container system. The providingof the volume may include mapping the volume to the container instance,such as by attaching and mounting a virtual volume, which is mapped tothe volume, to a host node for use by the container instance. Thecontainer system may then use the immutable container image from thevolume to run the container instance of the immutable container image inthe container system.

In some embodiments, in addition to providing the immutable containerimage to the container system, the storage system may provide persistentdata storage for use by the container system in running the containerinstance of the immutable container image in the container system, suchas when the container instance is a stateful container instance or isotherwise configured to write data to persistent storage. The storagesystem may provide persistent data storage in any suitable way. As anexample, the storage system may provide, to the container system, anadditional volume configured to provide persistent storage for thecontainer instance of the immutable container image in the containersystem. As another example, the volume that includes the immutablecontainer image and that is provided by the storage system to thecontainer system may comprise both an immutable layer including theimmutable container image (e.g., an immutable snapshot of the containerimage) and a writeable layer configured to provide persistent storagefor the container instance of the immutable container image in thecontainer system.

Various illustrative implementations of a container-aware storage systemand ways that the container-aware storage system may store, manage, andprovide immutable container images and optionally persistent datastorage to a container system, as well as ways that the container systemmay use the immutable container images and the persistent data storageto run container instances of the container images will now be describedin more detail with reference to several figures.

FIG. 5 illustrates an example configuration 500 of a container system502 and a container-aware storage system 504 (also referred to as thestorage system 504) configured to store, manage, and provide immutablecontainer images and optionally persistent data storage to the containersystem 502. The container system 502 and the storage system 504 may becommunicatively coupled and configured to communicate one with anotherin any suitable way. For example, the container system 502 and thestorage system 504 may communicate by way of an interface 506, which maybe any suitable interface (e.g., one or more APIs). The storage system504 may provide an interface that allows the storage system 504 tofunction as a container image storage layer as well as a persistentstorage layer. For example, the storage system may provide an interfaceconfigured to receive and interpret requests from the container system502, such as requests for container images, persistent storage, and/orvolumes.

The container system 502 may include an orchestration system 508 and acontainer runtime system 510. The orchestration system 508 may deploy,scale, and manage containerized applications within the container system502, such as by managing deployment of containerized applications toselect nodes of a cluster. The container runtime system 510 may providea container runtime environment on nodes of the cluster. When acontainerized application is deployed to a node for execution on thenode, the container runtime system 510 may perform operations to executethe containerized application within the container runtime environmenton the node.

Execution of a containerized application may include the containerruntime system 510 running, in the container runtime environment on thenode, a container instance 512 of a container image that represents thecontainerized application. To this end, the container runtime system 510may receive a request to run a container instance 512 of a containerimage and, in response to the request, access and use the containerimage associated with the request to run the container instance 512.Instead of accessing the container image from the local storage of thenode or accessing the container image over a network connection from acontainer image repository and loading the container image into thelocal storage of the node as is done in traditional container systems,the container system 510 may access the container image from the storagesystem 504.

In response to one or more requests from the container system 502, thestorage system 504 may provide a container image 514 and optionallypersistent storage 516 to the container system 502. The containerruntime system 510 may use the container image 514 to run the containerinstance 512 of the container image 514 in the container runtimeenvironment of the node. In addition, when the persistent storage 516 isprovided, the container runtime system 510 may use the persistentstorage 516 to persistently store data associated with running thecontainer instance 512 of the container image 514 in the containerruntime environment of the node.

The storage system 504 may be configured to function as a repository ofcontainer images that may receive, store, manage, and provide containerimages. For example, the storage system 504 may be configured to receiveand respond to push and pull requests to receive and provide containerimages. In certain embodiments, the storage system 504 is configured toreceive container images, store the container images as volumes, managethe volumes, and provide the volumes to the container system 502 for usein running container instances of the container images.

FIG. 6 illustrates an example of the storage system 504 receiving acontainer image 602 and storing the container image 602 as a volume 604.The container image 602 may be immutable and may include one or moreimage layers. In the illustrated example, the container image 602includes three image layers—image layer 1, image layer 2, and imagelayer 3. The image layers may have a hierarchical relationship. Forexample, image layer 3 may be the top-level image layer and may dependon and reference the next image layer, image layer 2, which may dependon and reference the next image layer, image layer 1. In someembodiments, each image layer may be considered an individual immutablecontainer image.

The storage system 504 may receive the container image 602 from anysuitable source and in any suitable way. In some embodiments, forexample, the container image 602 may be pushed from a container runtimesystem to the storage system 504 with a push operation.

The storage system 504 may receive and store the container image 602 asthe volume 604. This may include translating the container image 602into the volume 604. The translation may include opening each imagelayer in the container image 602 and writing data from the image layerinto an immutable volume snapshot. Specifically, in the illustratedexample, image layer 3 may be translated into snapshot 3 of the volume604, image layer 2 may be translated into snapshot 2 of the volume 604,and image layer 1 may be translated into snapshot 1 of the volume 604.The snapshots may be defined to have relationships that correspond tothe relationships between the image layers of the container image 602.For example, snapshot 3 may depend on and reference snapshot 2, andsnapshot 2 may depend on and reference snapshot 1. The relationshipsbetween the snapshots may be defined in any suitable way, including bysnapshots referencing other snapshots, metadata indicating relationshipsbetween snapshots, and/or the use of indexing or naming conventions thatindicate the relationships. In the illustrated example, all thesnapshots are associated with the volume 604 (e.g., are treated assnapshots of the volume 604). In some examples, volume 604 may beconsidered to be volume snapshot 3, which references volume snapshot 2,which references volume snapshot 1. The storage system 504 may store anymetadata that may be used to identify the volume 604 as being related tothe container image 602 (e.g., to identify that the volume 604 should beprovided in response to a request for the container image 602).

As used herein, a volume, such as the volume 604, may be an atomic unitof storage such as a logical drive or disk, which may be mapped tophysical storage. The volume is an identifiable unit of data storageconfigured to be managed by the storage system 504 (e.g., by applyingstorage operations to the volume) and accessed by a client.

With the container image 602 stored as the volume 604, the storagesystem 604 may apply one or more storage services of the storage system604 to the volume 604, including one or more of the storage servicesdescribed herein. For example, the volume 604 may be the subject ofstorage service operations associated with logical mapping (e.g.,attachment, mounting, etc.), deduplication, compression, replication,snapshotting, cloning, garbage collection, migration, disaster recovery,etc.

With the container image 602 stored as the volume 604, the storagesystem 604 may be ready to provide the volume 604 to the containersystem 502 for use by the container system 502 to run a containerinstance of the container image 602. In certain examples, the storagesystem 504 may provide the volume 604 to the container system 502 inresponse to a request for the container image 602. The request may befrom the container system 502, such as from the orchestration system 508or the container runtime system 510. The storage system 504 may detectthe request for the container image 602 in any suitable way, such as bydetecting a pull request or a run request associated with the containerimage 602 (e.g., a request to run a container instance of the containerimage 602).

The storage system 604 may provide the volume 604 to the containersystem 502 in any way that allows the container system 502 to run acontainer instance of the container image 602. In some examples, the waythat the storage system 604 provides the volume 604 to the containersystem 502 may depend on a configuration of the container system 502,such as on a configuration of the orchestration system 508 or thecontainer runtime system 510. In other examples, the storage system 604may provide the volume 604 to the container system 502 in any suitableway and how the container system 502 uses the volume 604 may depend on aconfiguration of the container system 502, such as on a configuration ofthe orchestration system 508 or the container runtime system 510.

In some embodiments, the storage system 504 may provide the volume 604to the container system 502 by attaching and mounting the volume 604 ona node on which the container instance will run. For example, thestorage system 504 may detect a request for the container image 602 on anode, identify the volume 604 as associated with the container image602, and provide the volume 604 by attaching and mounting the volume 604on the node, such as by attaching and mounting a virtual volume that ismapped to the volume 604.

In some examples in which the container image 602 includes multipleimage layers represented as snapshots of the volume 604 in the storagesystem 504, the mounting of the volume 604 may include the storagesystem 504 mounting the snapshots as individual volumes in an order thatrepresents the layered relationships of the image layers to one anotherin the container image 602. In other examples in which the containerimage 602 includes multiple image layers represented as snapshots of thevolume 604 in the storage system 504, the mounting of the volume 604 mayinclude the storage system 504 mounting a virtual volume that is mappedto the volume 604 in any suitable way, such as by being mapped to thehighest-level snapshot (snapshot 3) of the volume 604.

With the volume 604 mounted on the node, the container system 502 mayaccess the volume 604 and read container image data from the volume 604.For example, the container runtime system 510 may read container imagedata from the volume 604 and use the container image data to run thecontainer instance of the container image 602. In some embodiments, thecontainer runtime system 510 may do this by reading the container image602 from the volume 604 and loading the container image 602 into localstorage of the node (e.g., using a container pull operation). Thecontainer runtime system 510 may then use the container image 602 inlocal storage of the node to run the container instance of the containerimage 602 on the node. In other embodiments, the container runtimesystem 510 may not load the container image 602 into the local storageof the node and may instead access and use the container image 602 inthe mounted volume 604 to run the container instance of the containerimage 602 on the node. In such other embodiments, resources of the nodemay be conserved, such as by not having to use local storage of the nodeto store the container image 602. This may allow the node to be equippedwith less local storage resources than would be required to supportconventional container runtime environments and/or to use local storageresources for other purposes. By not loading container image data intolocal storage of the node, container startup times may be reducedcompared to conventional container runtime environments. In addition,the amount of data pulled onto the node may be reduced, which mayconserve network resources and further reduce container startup times.Such savings in resource usage and startup times may be significant whena reboot is performed (e.g., when a node reboots) and/or when containerinstances are moved from a source node to one or more other nodes (e.g.,due to an interruption on source node).

As mentioned above, in certain embodiments, the storage system 504 maybe configured to provide persistent data storage for container instancesrunning or that will run in the container system 502. In some examples,whether the storage system 504 provides persistent data storage for acontainer instance may depend on the needs of the container instance. Ifthe container instance will not use persistent storage, the containersystem 502 may not provide a request for persistent storage for thecontainer instance to the storage system 504, and the storage system 504may not provide persistent storage for the container instance. On theother hand, if the container instance is to use persistent storage, thecontainer system 502 may provide a request for persistent storage forthe container instance to the storage system 504, and the storage system504 may provide persistent storage for the container instance.

The storage system 504 may be configured to provide persistent storagefor the container instance in any suitable way. In some embodiments, thestorage system 504 provides a volume to which the container instance maywrite persistent data. The storage system 504 may attach and mount thevolume on a node for access by the container instance running on thenode.

In some embodiments, the volume provided for persistent storage may beprovided in addition to the volume 604 that contains data representingthe container image 602. Thus, the volume 604 may provide the containerimage 602 (in volume snapshot form) for use by the container system 502to run the container instance, and the additional volume may providepersistent storage to which the running container instance may writepersistent data and from which the running container instance may readpersistent data. FIG. 7A illustrates an example of the storage system504 providing, to the container system 502, 1) the volume 604 thatincludes data representing the immutable container image 602 and 2) anadditional, separate volume 702 configured to provide persistentstorage.

In some other embodiments, the volume provided for persistent storagemay be included in and/or provided together with the volume thatincludes the immutable container image. In such embodiments, forexample, the storage system 504 may provide, to the container system502, a single volume that comprises an immutable layer including datarepresenting the container image (this immutable layer may includemultiple sub-layers representing multiple snapshots) and a writeablelayer configured to provide persistent storage for use by the containerinstance. FIG. 7B illustrates an example of the storage system 504providing, to the container system 502, a volume 704 that includes animmutable layer 706 and a writeable layer 708. The immutable layer 706includes data representing the immutable container image 602 (e.g.,volume 604 that may include multiple sub-layers representing multiplevolume snapshots). The writeable layer 708 may include one or morewriteable volumes (e.g., writeable volume 710) configured to be used tostore persistent data for the container instance.

The storage system 504 may be configured to create the volume 704 thatincludes the writable layer 708 and the immutable layer 706 at anysuitable time. For example, the storage system 504 may create the volume704 in response to receiving one or more requests from the containersystem 502 for the container image 602 and persistent data storage. Inother examples, the storage system 504 may create the volume 704 inadvance of receiving one or more requests from the container system 502for the container image 602 and persistent data storage.

The storage system 504 may be configured to create the volume 704 thatincludes the writable layer 708 and the immutable layer 706 in anysuitable way. For example, the volume 704 may be created to represent ahierarchical chain of volumes that depend on one another in a way thatcorresponds to dependencies that will be used by the container system502 to run a container instance of the container image 602. In someembodiments, for example, the storage system 504 may provide thewriteable volume 710 defined to depend on the volume 604 (e.g., thewriteable volume 710 is a writeable layer on top of the volume 604).Volume 704 may be the writeable volume 710 or an additional volumedefined to depend on the writeable volume 710. The writeable volume 710may include one or more individual writeable volumes on top of thevolume 604. Accordingly, the volume 704 may define a stack of dependentvolumes that may be used by the container system 502 to run a containerinstance of the container image 602 and to read and write persistentdata while running the container instance. The stack path of the volume704 may mirror the stack path of the container instance. The volume 704may be attached and mounted as a single point from which an entirecontainer instance may start and run.

The container system 502 (e.g., the container runtime system 510) maymap any area in the container instance to a volume in the storage system504. For example, the container system 502 may attach a namespace of thevolume into a location in the container instance. Accordingly, thecontainer system 502 may access and use mounted volumes, such as volume704 or volumes 604 and 702, to run the container instance from thecontainer image 602 and to read and write persistent data. The containersystem 502 may provide an ephemeral layer, which may be a layer to whichephemeral data may be read and written while the container instance ifrunning. The ephemeral layer may be a layer on top of a volume providedby the storage system 504.

In embodiments in which a single volume, such as the volume 704,includes writeable and immutable container data in one place, thecontainer system 502 (e.g., the container runtime system 510) may nothave to individually request immutable container data and persistentstorage from different sources. For example, the container system 502may request to run a container instance of a container image, such as byrequesting a volume associated with the container image. In response,the storage system 504 may provide a single volume that includeswriteable and immutable container data in one place.

FIGS. 8-11 illustrate flowcharts depicting example methods. While theflowcharts depict illustrative operations according to some embodiments,other embodiments may omit, add to, reorder, combine, and/or modify anyof the operations shown in the flowcharts. In some implementations, oneor more of the operations shown in the flowcharts may be performed by astorage system such as the storage system 504, a container system suchas the container system 400 or 502, any components of the storage systemand/or the container system, and/or any implementation of the storagesystem and/or the container system.

As shown in FIG. 8 , an example method 800 includes: receiving, at 802,an immutable container image; storing, at 804, the container image as avolume; detecting, at 806, a request from a container system for thecontainer image; providing, at 808, the volume to the container system;and using, at 810, the volume to run a container instance in thecontainer system. Operations of the method 800 may be performed in anyof the ways described herein.

In certain embodiments, storing, at 804 of the method 800, may beperformed as illustrated in FIG. 9 . As shown in FIG. 9 , an examplemethod 900 includes: opening, at 902, an image layer in an immutablecontainer image (e.g., the immutable container image received at 802 inthe method 800); translating, at 904, the image layer to a volumesnapshot; storing, at 906, the volume snapshot; and storing, at 908,metadata for the volume snapshot. Operations of the method 900 may beperformed in any of the ways described herein. The metadata stored at908 may include any data associated with the volume snapshot, includingmetadata associating the volume snapshot to the container image (e.g.,metadata indicating an identifier of a container image and/or containerimage layer associated with the volume snapshot), any tags associatedwith the container image and/or container image layer associated withthe volume snapshot; an image hash for the container image and/orcontainer image layer associated with the volume snapshot, a timeassociated with receiving the container image and/or container imagelayer associated with the volume snapshot, an identifier for the volumesnapshot, one or more relationships with one or more other volumesnapshots (e.g., a reference from one snapshot to another snapshot),etc.

After 908, the method 900 further includes determining, at 910, whetherthere is another image layer in the container image. If there is anotherlayer, the method 900 is repeated starting again at 902. If there is notanother layer, the method 900 ends.

As shown in FIG. 10 , an example method 1000 includes: receiving, at1002, a request from a container system for a container image;identifying, at 1004, a volume corresponding to the container image;providing, at 1006, the volume to the container system; receiving, at1008, a request from the container system for persistent storage; andproviding, at 1010, a persistent storage volume to the container system1010. Operations of the method 1000 may be performed in any of the waysdescribed herein, including as described in reference to FIG. 7A. Therequests received at 1002 and 1008 may be received in parallel orsequentially in any suitable order.

As shown in FIG. 11 , an example method 1100 includes: receiving, at1102, a request from a container system for a volume that includes acontainer image and provides persistent storage; providing, at 1104, thevolume to the container system; and using, at 1106, the volume to run acontainer instance in the container system and to write persistent datafor the container instance. Operations of the method 1100 may beperformed in any of the ways described herein, including as described inreference to FIG. 7B.

As described herein, a volume may be specified to comprise one or morecontainer images. Such a volume may be used by a container system, acontainer runtime, a container orchestrator, and/or a storage system toefficiently deploy or redeploy containers among nodes of a cluster.Deployment of a container using a volume may benefit from advantages ofusing a volume, such as access to data through a volume mount point andaccessing container images associated with the volume over a storagearea network.

In some embodiments, the disclosed volumes, in addition to comprisingone or more container images, may comprise data storage. The datastorage may be data generated during operation of one or more containersassociated with the one or more container images within the datavolumes. In this way, a same volume may comprise both a container imageand persistent and/or ephemeral storage for data generated duringoperation of a container instance based on the container image. The datastorage may comprise, in addition to or instead of data generated duringoperating of the one or more containers, configuration data useable bythe one or more containers associated with the container images withinthe volume.

As described in greater detail below, an advantage of using a volumecomprising a container image includes a container runtime mounting thevolume on a node and accessing the container image. The containerruntime, based on access to the container image via the mounted volume,may create an instance of a container associated with the containerimage without copying the entire container image to the node. Otheradvantages may include faster spin up time when creating a containerinstance on a given node of a cluster and, in some embodiments,capability for using a same volume that comprises a container image asstorage usable by a container. One of more such features may allowcontainers to be efficiently deployed and/or redeployed (e.g., spunup/down) among nodes of a cluster of a container system.

In a container system, a container orchestrator may deploy one or morecontainers among one or more nodes of a cluster. In certainimplementations, a container orchestrator may operate to deploy and/orredeploy containers within a cluster without any knowledge of a volumebeing associated with container images. In other implementations, acontainer orchestrator may operate to deploy and/or redeploy containerswithin a cluster based on knowledge of one or more volumes that storecontainer images.

In some examples, subsequent to an initial deployment, a container mayfail and/or be redeployed for a variety of reasons. As one example for acontainer orchestrator determining redeployment of a container, based onmetrics regarding load balances among the nodes of a cluster, thecontainer orchestrator may determine to move a container operating on afirst node to a second node. In other examples, performance of acontainerized application, security threats, system upgrades, increasesor decreases in compute resources, failure of a node, among otherreasons may be used by a container orchestrator to redeploy a container,such as by spinning down the container on one node and spinning up thecontainer on one or more other nodes.

Failure or redeployment of containers may occur frequently enough thatfaster movement of a container among nodes of a container system mayresult in appreciable improvements in container system performance orresource utilization. The disclosed volumes that comprise containerimages may be a basis for quicker deployment of containers. Deploymentsor redeployments may be quicker based on having local access to astorage system that stores volumes associated with container images. Incontrast, traditional systems may access a repository over the Internetto access container images.

Further, because the disclosed volumes may use logical structures andapplication programming interfaces that are compatible with existingcomponents of a container system that use storage systems, an amount ofmodification of the components of a container system to use thedisclosed volumes may be reduced. For example, a container runtime mayhave APIs to communicate with storage systems to identify, mount, read,write, or otherwise modify characteristics of data associated with avolume.

As another example, based on a container runtime accessing a containerimage associated with a volume similarly to accessing storage contentwithin a volume, bursts of network traffic within a container system maybe avoided. For example, network traffic may be managed based on acontainer runtime accessing portions of a container image over ascheduled period of time instead of copying over a full container imagefrom a remote network location to local memory of a node where acontainer instance is created.

FIG. 12A illustrates an example storage system 1202 that stores a volume1204 comprising a container image according to some implementations. Thestorage system 1202 may be implemented similarly to one or more of thestorage systems described with respect to FIGS. 1A-11 .

The volume 1204 may comprise volume metadata 1206 and a container image1210. In this example, the volume metadata 1206 includes containermetadata 1208 that describes one or more layers 1212 of the containerimage 1210. Metadata 1206 may describe a structured collection of dataobjects that represent logical relationships between layers of thecontainer image 1210. In certain implementations, different types oflogical relationships may be implemented. In this example, the logicalrelationships between image layers 1212 may be structured as a directedacyclic graph.

In certain implementations, a dependency between image layers 1212 maybe represented by a directed link between the data objects associatedwith image layers 1212. For example, image layer 1212-1 may be a baselayer that is not dependent on another image layer, image layer 1212-2may be dependent on image layer 1212-1, and image layer 1212-3 may bedependent on image layer 1212-2. A dependency between image layers mayinclude one or more references from a first, dependent, image layer to asecond image layer.

The one or more layers 1212 may be image layers as described withrespect to FIG. 6 . In this example, the container image 1210 includesimage layers 1212-1-1212-3. In other examples, as described with respectto FIG. 12B, the volume metadata 1206 may include both containermetadata 1208 and storage metadata.

FIG. 12B illustrates an example storage system 1202 that stores a volume1204 comprising a container image according to some implementations.

As described with respect to FIG. 12A, a volume 1204 may comprise volumemetadata 1206 and a container image 1210. In this example, the volume1204 additionally comprises storage metadata 1220 and container storage1222-1-1222-M. In certain embodiments, a container runtime may mount afirst volume that provides access to the container image 1210, mount asecond volume such as volume 1224 that provides access to the containerstorage 1221-1, and mount an M^(th) volume such as volume 1228 thatprovides access to the container storage 1222-M. In this example, themounted volumes associated with container storage 1221 may be usedduring operating of the container to store data. Data stored within avolume associated with container storage 1221 may be persisted in theevent that the container is redeployed, fails, or otherwise becomesunavailable.

Storage metadata 1220 may describe a dataset associated with the volume1224, and in examples, one or more snapshots 1226 of the dataset. Thestorage metadata 1220 may represent volume 1224 based on directedacyclic graph or in any other suitable way.

In certain implementations, the volume 1224 may be specified by thestorage metadata 1220 as a directed acyclic graph, such as a B-tree,where leaves of the storage metadata 1220 representation may includepointers to stored data objects. A volume address, or volume referenceand offset, may be used to navigate the storage metadata 1220representation of the volume 1224. For example, the storage metadata1220 representation of the volume 1224 may include data objects as leafnodes, and intermediate nodes may be references to leaf nodes. Thevolume 1224 may comprise an aggregate of the data objects at the leafnodes of the storage metadata 1220, where the organization of the dataobjects is specified by the intermediate nodes. A data object may be alogical extent, where a logical extent may be any specified size, suchas 1 MB, 4 MB, or some other size.

A snapshot 1226 may be specified by a metadata representation similar tothe metadata representation of a volume. For example, the snapshot 1226may be a directed acyclic graph, where the snapshot 1226 includesreferences and data objects that are associated with one or more changesto the dataset of the volume 1224. A given snapshot may comprise dataobjects that are associated with the one or more changes to the volume1224 dataset in addition to one or more references to the metadatarepresentation of the volume 1224 associated with data objects that havenot been changed by the one or more changes associated with the givensnapshot. Based on the references from the given snapshot to the volume1224 dataset, the given snapshot may be considered dependent on thevolume 1224.

As depicted in this example, storage metadata 1220 describes containerstorage 1222-1-1222-M. Container storage 1222-1 represents the volume1224 and snapshots 1226-1 and 1226-2 of the volume 1224. Snapshot 1226-1is dependent on the volume 1224, and snapshot 1226-2 is dependent onsnapshot 1226-1. Container storage 1222-M represents volume 1228 andsnapshot 1230-1 of the volume 1228. Snapshot 1230-1 is dependent on thevolume 1228.

As described with respect to FIGS. 12A-12B, various features of a volumethat comprises one or more container images, and in some examples, oneor more storage volumes are described. As described with respect toFIGS. 13-15 , such a volume comprising one or more container images maybe implemented within various computing environment architectures.Within various architectures, a container orchestrator, a containerruntime, and/or a storage system may have different roles and featureswith respect to deployment or redeployment of a container among nodes ofa container system. A first example, described with respect to FIG. 13 ,describes a storage system implemented independently of a containersystem. In this example, the storage system may provide volumescomprising container images to either a container runtime or containerorchestrator operating within the container system. A second example,described with respect to FIG. 14 , describes a cluster that comprises astorage system, such as any of the storage systems described withrespect to FIGS. 5-11 . In this example, the cluster and the storagesystem operate within a cloud environment provided by a same cloudservices provider, and utilize storage resources that are outside of thecluster and provided by the cloud services provider. The storageresources could be implemented in the cluster in other implementations.A third example, described with respect to FIG. 15 , describes acontainer system where a container runtime uses a volume to accesscontainer images, and where the volume is obtained from a cloud-basedobject storage operating as a repository for volumes comprisingcontainer images. In the following figures, components with referencenumbers described with respect to previous figures are implementedsimilarly.

FIG. 13 illustrates an example computing environment 1300 that uses avolume 1204 comprising a container image 1210 according to someimplementations.

In computing environment 1300, a container orchestrator, such asorchestration system 508 described with respect to FIG. 5 , may deployor redeploy a container among nodes 420 of a cluster 408. The containerorchestrator may communicate information about a deployment of thecontainer to a container runtime 1302 running on a node 420, where thecontainer runtime may include a data volume module 1304 that isconfigured to interface with (e.g., interpret) volumes comprisingcontainer images. Based on the received information, the containerruntime 1302 may communicate with a storage system 1306 to request acontainer image 1210 associated with the container. The request may bemade in any suitable way, such as by sending, to the storage system1306, a request for the container image 1210 and/or the volume 1204,where the volume 1204 may be used to access the container image 1210.The storage system 1306 may generate, store, and/or provide volumescomprising one or more container images. In this example, as describedabove, the storage system 1306 is implemented outside of the cluster 408and outside of and/or independent from a container system in which thecluster 408 operates.

In this example, the container runtime 1302 accesses a volume 1204within the storage system 1306. The container runtime 1302 may accessthe container image 1210 associated with the volume 1204 to create aninstance of the container image 1210 on a node 420-1 within the cluster408. To create the instance of the container image 1210, the containerruntime 1302 may request the volume 1204 from a storage system 1306. Thecontainer runtime 1302 may be implemented similarly to the containerruntime 402, where the container runtime 1302 may comprise fewer,additional, or all features described above with respect to thecontainer runtime 402. In this example, the container runtime 1302comprises the data volume module 1304 configured to interpret and/orotherwise interface with volumes comprising container images.

The data volume module 1304 may be configured to access one or moreimage layers of a container image from a volume 1204 that comprises oneor more container images 1210. The data volume module 1304 may implementany of the operations described above with respect to FIGS. 5-11 ,including generating container images within a volume, accessing acontainer image within a volume, modifying a container image within avolume, moving a volume from one node to another node within a containersystem, among others.

The storage system 1306, similar to storage system 504 described withrespect to FIGS. 5-11 , may use an identifier of the container image1210 to select the volume 1204 associated with the identifier. Thestorage system 1306 may be implemented similarly to storage systems 402and 504, where the storage system 1306 may comprise fewer, additional,or all features described above with respect to storage systems 402 and504.

Continuing with this example, to access the volume 1204, the containerruntime 1302 may send a request 1308-1 to the storage system 1306 forthe volume 1204. The request 1308-1 may indicate an identifier of thecontainer image 1210. In response to the request 1308-1, the storagesystem 1306 may provide volume data 1310-1, where the volume data 1310-1is associated with the volume 1204. The container runtime 1302 may usethe volume data 1310-1 to mount the volume 1204 to access the containerimage 1210. In some examples, volume data may be a network path or anetwork location and credentials, such as a username and/or password toan account associated with one or more storage resources. Based onmounting and accessing the volume 1204, the container runtime 1302 maycreate the instance of the container image 1210 in response to adeployment of an application associated with the container image 1210.

In contrast, in traditional systems, a container image repository isused to access a container image, where the container image is notincluded as part of a volume. A container image repository may be onesuch as a Docker™ image repository, among others. In certainimplementations, a node 420 on which the container image 1210 is to beinstantiated may have a connection to the storage system 1306 over astorage area network. A storage area network may be a Fibre Channel,iSCSI, NVMe, or some other storage area network implementation, such asstorage area network 158 described with respect to FIGS. 1A-1D. Accessto the container image over a storage area network may avoid impactingnetwork activity of other container system processes that may be usingother types of network connections. Further, access to a container imagevia a storage area network using the volume data 1310-1 is in contrastto traditional container runtimes that may access container images overa wide area network, such as the internet, to reach a repository.

In certain implementations, the container orchestrator may deploy a sameapplication on multiple nodes 420 of the cluster 408. For example, adeployment configuration file may specify a replication factor of Z.Based on the replication factor of Z, the container orchestrator maydeploy Z instances of the container image 1210 associated with theapplication among Z nodes 420. Similar to the description of containerruntime 402 with respect to FIGS. 5-11 , based on a container runtime1302 mounting a volume to access a container image, a network load fromtransmission of Z copies of the container image 1210 is avoided.

In this example, to deploy a first instance of the container image 1210,container runtime 1302 on node 420-1 issues the request 1308-1 andreceives volume data 1310-1. Similar to the description of containerruntime 402 with respect to FIGS. 5-11 , the container runtime 1302 onnode 420-1 may use the volume data 1310-1 to mount the volume 1204 toaccess and use the container image 1210 to run a container instance onthe node 420-1, thereby spinning up a container associated with thecontainer image 1210 on the node 420-1. Similarly, to deploy instance Zof the container image 1210, container runtime 1302 on node 420-N issuesrequest 1308-Z and receives volume data 1310-Z. The container runtime1302 on node 420-N may use the volume data 1310-Z to mount the samevolume 1204 to access and use the container image 1210 to run acontainer instance on the node 420-N, thereby spinning up anothercontainer associated with the container image 1210 on the node 420-N.

FIG. 14 illustrates an example computing environment 1400 that uses avolume 1204 comprising a container image 1210 according to someimplementations.

In computing environment 1400, similar to computing environment 1300, acontainer orchestrator may deploy or redeploy a container among nodes420 of a cluster 408. The container orchestrator may communicateinformation about a deployment of the container to a container runtime1302 running on a node 420. Based on the received information, thecontainer runtime 1302 may communicate with a storage system 1402 torequest a container image 1210 associated with the container. Therequest may be made in any suitable way, such as by sending, to thestorage system 1306, a request for the container image 1210 and/or thevolume 1204, where the volume 1204 may be used to access the containerimage 1210. The storage system 1402 may generate, store, and/or providevolumes comprising one or more container images. However, in contrast tothe computing environment 1300, in computing environment 1400, thestorage system 1402 may be implemented among one or more nodes 410 and420 of the cluster 408.

The storage system 1402 may be implemented similarly to storage system402 and 504, where the storage system 1402 may comprise fewer,additional, or all features described above with respect to storagesystem 402 and 504.

Deployment and redeployment of containers may be performed by acontainer orchestrator, container runtime 1302, and/or storage system1402 in computing environment 1400 similarly to the containerorchestrator, container runtime 1302, and/or storage system 1306described with respect to FIG. 13 .

FIG. 15 illustrates an example computing environment 1500 that uses avolume 1204 comprising a container image 1210 according to someimplementations.

In computing environment 1500, similar to computing environment 1300, acontainer orchestrator may deploy or redeploy a container among nodes420 of a cluster 408. The container orchestrator may communicateinformation about a deployment of the container to a container runtime1302 running on a node 420. As described with respect to computingenvironment 1300, based on the received information, the containerruntime 1302 may communicate with a storage system 1306 to request acontainer image 1210. By contrast to the above example, in this example,the container runtime 1502 may use volume data to directly or indirectlyaccess a cloud-based object store 348 that stores the volume 1204. Insome examples, the cloud-based object store 348 may be provided by athird-party cloud services provider, where the cloud-based object store348 may serve as a repository for volumes comprising container imageswithout comprising features described with respect to thecontainer-aware storage system 504. In this example, volume data may beused to mount a volume, or may comprise volume metadata, includingcontainer metadata and/or storage metadata. Based on access to thevolume 1204, the container runtime 1502 may access a container image1210 to create a container instance, as described with respect to FIG.13 .

Continuing this example, the container orchestrator may interpret aconfiguration file to determine that a container image is associatedwith a volume stored within a data store, such as the cloud-based objectstorage 348. For example, the configuration file may comprise anidentifier that is associated with one or more container images withinthe volume 1204. The configuration file may comprise informationindicative of deployment of the container within the container system,including numbers of replicas, storage parameters, among otherconfiguration information.

As described with respect to FIG. 12A, a container orchestrator mayredeploy a container by spinning down a container on one node (e.g., afirst node) and spinning up the container on another node (e.g., asecond node). The container orchestrator may spin down a container onthe first node in any suitable way, including using traditionalmechanisms. In some examples, the spinning down may include unmounting avolume used by the container, such as by unmounting a volume comprisingone or more container images used to run the container on the firstnode. The container orchestrator may spin up the container on the secondnode as described herein with respect to using a volume comprising oneor more container images. For example, the container orchestrator maydetermine to redeploy the container from the first node to the secondnode of the container system. Based on determining to redeploy thecontainer, the container orchestrator may provide volume data indicativeof the volume to a container runtime on the second node.

In certain implementations, the configuration file may comprise a mountpoint on the node 420 where the container runtime 1502 may access thedata store via the mounted volume 1204. The container runtime 1502,based on access to the volume 1204 via the mount point, may use the datavolume module 1304 to access one or more container images 1210associated with the volume 1204.

In other examples, the container orchestrator may provide the containerruntime 1502 with an identifier for the container image 1210. Based onthe identifier, the container runtime 1502 may request volume data froma storage system storing a volume associated with the identifier, asdescribed with respect to FIGS. 13-14 . For example, the containerruntime 1302 may send a request 1308-1 to the storage system 1306 andreceive the volume data 1310-1 that may be used to access the volume1204. In some examples, volume data may comprise an identifier for thecontainer image 1210 and may be used by the container runtime 1502 toaccess the volume 1204.

FIGS. 16-18 illustrate flowcharts depicting example methods that use avolume 1204 comprising a container image 1210 according to someimplementations. While the flowcharts 1600,1700, and 1800 depictillustrative operations according to some embodiments, other embodimentsmay omit, add to, reorder, combine, and/or modify any of the operationsshown in the flowcharts. In some implementations, one or more of theoperations shown in the flowcharts may be performed by a storage systemsuch as the storage system 504, a container system such as the containersystem 400 or 502, any components of the storage system and/or thecontainer system, and/or any implementation of the storage system and/orthe container system. For example, the method shown in FIG. 16 may beperformed by a storage system 1602, which may include any of theillustrate storage systems described herein; the method shown in FIG. 17may be performed by a container orchestrator 1702, which may include anyof the illustrative container orchestrators described herein; and themethod shown in FIG. 18 may be performed by a container runtime 1801,which may include any of the illustrative container runtimes describedherein.

As shown in FIG. 16 , an example method includes: receiving, at 1604, arequest comprising an identifier indicative of a container image, wherethe request is associated with a node of a cluster of a containersystem, and where the storage system is associated with the containersystem; identifying, at 1606, based on the identifier, a volumecomprising one or more layers of a container; and providing, at 1608, inresponse to the request, volume data indicative of the volume.

Receiving, at 1604, the request comprising the identifier indicative ofa container image may be implemented as described with respect to FIGS.12A-15 . For example, as described with respect to FIG. 13 , the storagesystem 1306 receives a request 1308-1 from a container runtime

Identifying, at 1606, based on the identifier, the volume may beimplemented as described with respect to FIGS. 12A-15 . For example, asdescribed with respect to FIG. 13 , the storage system 1306 may usevolume metadata 1206 and/or container metadata 1208 to identify acontainer image 1210 and/or corresponding volume based on an identifier.

Providing, at 1608, in response to the request, volume data indicativeof the volume may be implemented as described with respect to FIGS.12A-15 . For example, as described with respect to FIG. 13 , the storagesystem 1306 provides volume data 1310-1 to the container runtime 1302.

As shown in FIG. 17 , an example method includes: determining, at 1704,an identifier indicative of a container image, where the container imageis associated with a node of a cluster of a container system;identifying, at 1706, based on the identifier, a volume comprising oneor more layers of a container; and providing, at 1708, to a containerruntime within the container system, volume data indicative of thevolume.

Determining, at 1704, the identifier indicative of the container imagemay be implemented as described with respect to FIGS. 12A-15 . Forexample, as described with respect to FIG. 15 , a container orchestrator1702 may interpret a configuration file to determine an identifier ofthe container image 1210.

Identifying, at 1706, based on the identifier, the volume comprising oneor more layers of a container may be implemented as described in withrespect to FIGS. 12A-15 . For example, with respect to FIG. 15 , acontainer orchestrator may identify the volume 1204 by requesting volumedata and providing the identifier to the cloud-based object store 348.

Providing, at 1708, to a container runtime within the container system,volume data indicative of the volume may be implemented as describedwith respect to FIGS. 12A-15 . For example, based on the volume data,the container orchestrator may provide the container runtime 1502 with amount point associated with the volume 1204 or with the volume data.

As shown in FIG. 18 , an example method includes: receiving, at 1802, bya container runtime within a container system, an identifier indicativeof a container image; identifying, at 1804, by the container runtime andbased on the identifier, a volume comprising one or more layers of acontainer; and, at 1806, accessing, based on mounting the volume on anode of the container system, the container image.

Receiving, at 1802, the identifier indicative of the container image maybe implemented as described with respect to FIGS. 12A-15 . For example,as described with respect to FIG. 15 , the container runtime 1502 mayreceive an identifier indicative of a container image from a containerorchestrator.

Identifying, at 1804, based on the identifier, the volume comprising oneor more layers of a container may be implemented as described withrespect to FIGS. 12A-15 . For example, as described with respect to FIG.13 , the container runtime 1302 may send a request 1308-1 to the storagesystem 1306 and receive the volume data 1310-1.

Accessing, at 1806, based on mounting the volume on a node of thecontainer system, the container image may be implemented as describedwith respect to FIGS. 12A-15 . For example, as described with respect toFIG. 13 , the container runtime 1302 may use the volume data 1310-1 tomount the volume 1204 on node 420-1, where the mounted volume 1204 maybe accessed using one or more storage system operations.

In certain implementations, a recovery process determines to recover acontainer operating on a node of a container system and recovers thecontainer onto a recovery node by using a volume comprising a containerimage for the container. The volume may be stored in a storage systemand may be instantiated on the recovery node by a container runtime thatmounts the volume and accesses the container image over a storage areanetwork. The recovery process may be implemented as part of a storagesystem, a container orchestrator, or as part of both a storage systemand container orchestrator.

The recovery process may determine to recover the container based on thedetection of one or more events that have been defined as events thatwill or may lead to recovery of a container. For example, the recoveryprocess may detect one or more of: a container failure, a performancedegradation associated with a container or containerized application, ora security threat associated with a container. In response to detectingone or more of these events, the recovery process may determine torecover a container affected by the event(s).

In response to determining to recover a container, the recovery processmay initiate recovery of the container using various techniques. As afirst example, the recovery process may provide a container runtime on arecovery node with volume data, where the container runtime on therecovery node may use the volume data to access a container image usableto instantiate the container on the recovery node. As a second example,the recovery process may provide an identifier associated with acontainer image to the container runtime on the recovery node, where thecontainer runtime may use the identifier to obtain volume data for thecontainer image. The container runtime may use the volume data to mounta volume including the container image and, through the mounted volume,access and use the container image to instantiate the container on therecovery node.

Based on using a volume to recover a container, the recovery of thecontainer may be performed faster than if the recovery of the containerwere performed using traditional techniques such as downloading thecontainer image to local storage on the recovery node. In addition,based on using a volume to recover a container, the recovery process mayuse a storage area network, which may avoid the transfer of containerimage data onto the recovery node from affecting other container systemnetwork traffic using other communication networks. In contrast, intraditional container systems, recovery of a container may usecommunication networks used by other network processes and applications.Use of communication networks used by other container systemapplications may degrade performance of the applications as a containerruntime accesses a container image on a remote repository. The use ofvolumes to recover containers may help prevent or reduce the highdemands that are conventionally placed on resources (e.g., networkresources) when a large number of containers are recovered from one node(e.g., due to a node failure or lack of performance on the node) to oneor more recovery nodes.

FIG. 19A illustrates an example computing environment 1900 in whichcontainer recovery using volumes comprising container images isimplemented according to some implementations. The storage system 1306may be implemented similarly to one or more of the storage systemsdescribed with respect to FIGS. 1A-18 . The container system may beimplemented similarly to one or more of the container systems describedwith respect to FIGS. 1A-18 .

As depicted in FIG. 19A, within a cluster 408 of a container system,nodes 420-1-420-L may comprise respective container instances512-1-512-L. In this example, the container instance 512-1 may comprisea containerized application 404-1 and the container instance 512-L maycomprise a containerized application 404-L.

A recovery process module 1902 may implement the recovery process. Therecovery process module 1902 may be implemented as part of either acontainer orchestrator, a storage system such as the storage system1306, or a combination of a container orchestrator and a storage system.The container orchestrator, such as the container orchestratorimplemented by orchestration system 508, may be implemented as describedabove with respect to FIG. 5 or in any other suitable way.

In certain implementations, the recovery process may determine, based ondata such as node data and/or container data, to recover a containeroperating on a first node onto a second node. In some examples, suchdata may comprise telemetry data 1904, which may include any informationabout a node and/or containers running on the node and may be providedfrom nodes 420 to recovery process module 1902. For example, node datamay comprise a status of a given node, such as an indication of failure,an indication of an error state, or some other indication associatedwith a status of the given node.

Telemetry data 1904 may indicate storage resource metrics for a node,such as IOPS, latencies, storage capacity, load data, among othermetrics associated with operation of a given node. Based at least on thetelemetry data 1904, the recovery process may determine that aparticular node has failed, does not satisfy a performance threshold,does not satisfy a service level agreement, or is otherwise identifiedfor recovery of one or more containers operating on the node. Continuingthis example, based on determining to recover a container, the recoveryprocess may initiate recovery of one or more containers on the node ontoone or more recovery nodes. A performance threshold may be indicative ofa particular number of IOPS, a latency value, storage capacity data,load data, or any other metric indicated by the telemetry data 1904.

Monitor 1906 may be used to determine telemetry data. The monitor 1906may be implemented as part of either a container orchestrator or astorage system 1306. The monitor 1906 may provide the telemetry data1904 to the recovery process. The monitor 1906 may determine telemetrydata 1904 by receiving the telemetry data 1904 from one or more monitoragents 1908 running on the nodes 420.

Monitor agent 1908 may determine the telemetry data 1904 in any suitableway, including using one or more traditional techniques. For example,the monitor agent 1908 may use services such as DZoneTM NR1TM GrafanaTM,KubernetesTM tools, among other solutions. The monitor agent 1908 mayprovide the telemetry data 1904 to the container orchestrator and/or thestorage system 1306 via a node 420 network interface 1910.

Continuing this example, based on determining to recover the containeroperating on the first node onto the second node, the recovery processmay initiate the recovery using various techniques.

As a first technique, the recovery process may determine, based on acontainer image associated with the container, volume data indicative ofa volume comprising the container image. For example, the recoveryprocess may be part of a container orchestrator and may request volumedata from the storage system 1306, where the request may indicate therequested container image. A response to the request may be receivedfrom the storage system 1306 and may indicate volume data for a volumecomprising the container image. Examples of use of the container imageto determine an associated volume is described with respect to FIGS.5-11 . In certain examples, the container orchestrator may determine thevolume data based on a deployment file that specifies the containerimage. The container orchestrator may request the volume data from thestorage system 1306, where the request may indicate the container imageand a response received from the storage system 1306 may indicate volumedata for the volume comprising the container image.

Based on the volume data, the recovery process may initiate the recoveryof the container from the first node to the second node such as byproviding the volume data to a container runtime on the second node. Inthis example, the container runtime on the second node may use thevolume data to mount the volume comprising the container image, wherethe volume is stored by the storage system 1306.

As described with respect to FIGS. 5-18 , based on the storage system1306 storing the volume, the volume may be accessed over a storage areanetwork. In some examples, the volume may, in addition to comprising thecontainer image, comprise storage associated with an applicationoperating within the container. The storage may comprise stateinformation of the application, files, data objects, or other data usedby the application that may be used to spin up the container and theapplication on the second node in a state that replicates the containerand application as it existed on the first node. Use of a volume toinclude both container images and storage is described in greater detailwith respect to FIGS. 5-18 .

As a second technique, the recovery process may determine a containerimage associated with the container, where a volume comprises thecontainer image. For example, the recovery process may access adeployment file associated with the container, where the deployment filemay include an indication of the container image. In this example, therecovery process may initiate, based on the container image, recovery ofthe container from the first node to the second node by providinginformation such as an identifier for the container image to a containerruntime operating on the second node. As described with respect to FIGS.5-18 , the container runtime may use the information such as theidentifier to request the container image or volume data associated withthe volume comprising the container image from the storage system 1306,where the request may indicate the container image (e.g., by includingan identifier and/or other information indicating the container image).

FIG. 19B illustrates another example computing environment 1900 in whichcontainer recovery using volumes comprising container images isimplemented according to some implementations.

As depicted in FIG. 19B, the recovery process may determine to recover acontainer 512-1 operating on a first node 420-1 onto a second node,where the second node is depicted as recovery node 1912.

In this example, a containerized application 404-1 may be operatingwithin a container instance 512-1, and a volume 1204 may comprise acontainer image 1210 associated with the container instance 512-1. Thevolume 1204 may also comprise container storage 1222 associated withoperation of the containerized application 404-1. As the containerizedapplication 404-1 operates, it may generate resource data 1914comprising one or more of: state data, files, data objects, or othertypes of data generated and written to container storage 1222 by thecontainerized application 404-1.

As described with respect to FIG. 19A, initiating recovery by therecovery process may comprise the container runtime 1302 on the recoverynode 1912 using volume data 1916 to mount volume 1204 onto the recoverynode 1912. Based on the mounted volume 1204 on the recovery node, thecontainer runtime 1302 may create the container instance 512-1 andinitiate the containerized application 404-1 based on accessing thecontainer image 1210 and accessing the resource data 1914 stored withinthe container storage 1222.

FIG. 20 illustrates a flowchart 2000 depicting a method for containerrecovery using volumes comprising container images according to someimplementations. While the flowchart 2000 depicts illustrativeoperations according to some embodiments, other embodiments may omit,add to, reorder, combine, and/or modify any of the operations shown inthe flowchart.

In some implementations, one or more of the operations shown in theflowchart may be performed by a storage system such as the storagesystem, a container system such as the container system 400 or 502, anycomponents of the storage system 1306 and/or the container system (e.g.,a container orchestrator and/or a container runtime), and/or anyimplementation of the storage system and/or the container system.

As shown in FIG. 20 , an example method includes: determining, at 2002,by a recovery process and based on node data, to recover a containeroperating on a first node onto a second node, where a container image isassociated with the container; determining, at 2004, by the recoveryprocess and based on the container image, volume data indicative of avolume comprising the container image; and initiating, at 2006, by therecovery process and based on the volume data, recovery of the containerfrom the first node onto the second node.

In this example, operations of the flowchart 2000 may be performed inany of the ways described herein, including as described in reference toFIGS. 19A and 19B.

FIG. 21 illustrates a flowchart 2100 depicting a method for containerrecovery using volumes comprising container images according to someimplementations. While the flowchart 2100 depicts illustrativeoperations according to some embodiments, other embodiments may omit,add to, reorder, combine, and/or modify any of the operations shown inthe flowchart.

In some implementations, one or more of the operations shown in theflowchart may be performed by a storage system such as the storagesystem, a container system such as the container system 400 or 502, anycomponents of the storage system 1306 and/or the container system (e.g.,a container orchestrator and/or a container runtime), and/or anyimplementation of the storage system and/or the container system.

As shown in FIG. 21 , an example method includes: determining, at 2102,by a recovery process and based on node data, to recover a containeroperating on a first node onto a second node, where a container image isassociated with the container; determining, at 2104, by the recoveryprocess, a container image associated with the container, where a volumecomprises the container image; and initiating, at 2106, by the recoveryprocess and based on the container image, recovery of the container fromthe first node onto the second node.

In this example, operations of the flowchart 2100 may be performed inany of the ways described herein, including as described in reference toFIGS. 19A and 19B.

In some embodiments, a recovery process is provided that prioritizescontainer layers in a manner that, compared to other container recoveryprocesses, may reduce downtime and/or may more quickly or efficientlyrecover containers from one compute location to another computelocation, such as from one cluster of compute nodes to another clusterof compute nodes. For example, a recovery process may determine torecover containerized applications running on first cluster to a secondcluster, meaning that the containerized applications will be deployedand run on the second cluster starting at a state of the containerizedapplications as they existed on the first cluster at a point in time.The determination may be made in any suitable way, such as by receivinga recovery request from an element of a container system (e.g., anorchestrator) or from an element of a container storage system thatprovides persistent storage to the container system. The recovery may berequested for any suitable reason(s), such as because of performance orpredicted performance of the first cluster, utilization load of thefirst cluster, failure of one or more components of the first cluster,etc.

FIGS. 22A-22E illustrate an example configuration 2200 in which arecovery process is applied in accordance with some embodiments of thepresent disclosure. The recovery process depicted in these figures isillustrative. Other examples of the recovery process are contemplatedand may include additional and/or alternative operations.

As shown in FIGS. 22A-22E, the configuration 2200 includes a firstcluster 2202-1 of compute nodes 2204-1 through 2204-K (“first computenodes 2204”) and a second cluster 2202-2 of compute nodes 2204-M through2204-X (“second compute nodes 2204”). Containerized applications 2206-1through 2206-L (“containerized applications 2206”) are running on thefirst set of compute nodes 2204 in the first cluster 2202.

A first storage system 2208-1 is a container-aware storage system thatprovides storage services to the containerized applications 2206 runningon the first cluster 2202-1. To this end, the first storage system2208-1 maintains a first dataset 2210-1 that includes immutable layers2212 and mutable layers 2214. In some implementations, the immutablelayers 2212 and mutable layers 2214 are stored and managed as volumessuch as described herein. In some implementations, the first storagesystem 2208-1 may include or be implemented as any of the illustrativecontainer storage systems described herein.

The immutable layers 2212 include container layers, such as layers ofcontainer images (e.g., an operating system layer, and applicationlayer, etc.), that do not change or only seldomly change. For example,the container images may be unchangeable by normal data writes and canonly be changed by code updates, such as an update to an operatingsystem layer or application layer. The first storage system 2208-1 mayprovide the immutable layers 2212 of container images to the firstcompute nodes 2204 for use by the first compute nodes 2204 to runinstances of the container images, such as in any of the ways describedherein.

The mutable layers 2214 include container layers that may change duringnormal operations, such as when IO requests write data to the mutablelayers 2214. The first storage system 2208-1 may provide the mutablelayers 2214 to the first compute nodes 2204 as persistent storage foruse by the first compute nodes 2204 to read and write data. The firstcompute nodes 2204 may access, from the first storage system 2208-1, anduse the immutable layers 2212 and the mutable layers 2214 to run thecontainerized applications 2206 on the first cluster 2202-1.

In certain embodiments, one or more of the immutable layers 2212 mayinclude or be implemented as immutable layer 706 shown in FIG. 7B, andone or more of the mutable layers 2214 may include or be implemented aswritable layer 708 shown in FIG. 7B. Immutable layers 2212 and mutablelayers 2214 may be implemented in any other suitable way in otherembodiments.

Configuration 2200 may further include a recovery process module 2220configured to provide a recovery process, which may be any recoveryprocess described herein. The recovery process module 2220 may beimplemented in the storage system 2208 as shown, in any other suitablestorage system (container-aware storage system 504), and/or by anyelement(s) of the first cluster 2202-1 and/or a container system such ascontainer system 400.

In some examples, the recovery process may perform operations to recoverthe container applications 2206 from the first cluster 2202-1 to thesecond cluster 2202-2. The recovery process may be performed in a mannerthat prioritizes layers in the first dataset 2210-1 relative to oneanother in a manner that provides one or more benefits, which mayinclude faster recovery time, less downtime, more efficient processing,etc.

The recovery process may perform operations to intelligently prepare inadvance for a future recovery event. For example, the recovery processmay identify a set of the immutable layers 2212 included in the firstdataset 2210-1 and copy the identified set of immutable layers 2212 tothe second cluster 2202-2. The copying of the identified set ofimmutable layers 2212 to the second cluster 2202-2 is represented bydashed line 2222 in FIG. 22A.

The identified set of immutable layers 2212 may include container imagesor layers of container images included in the dataset 2210-1 maintainedby the first storage system 2208-1, which layers may be represented aslayers of volumes, such as snapshots of volumes as described above. Insome examples, the recovery process may identify and include all suchimmutable layers 2212 of the dataset 2210-1 in the identified set ofimmutable layers 2212. In other examples, the recovery process mayidentify and include only unique immutable layers 2212 of the dataset2210-1 in the identified set of immutable layers 2212. Thus, theidentified set of immutable layers 2212 may include only unique,non-duplicative instances of immutable layers 2212. The recovery processmay identify the set of immutable layers 2212 in any suitable way. Forexample, the immutable layers 2212 may be labeled (e.g., as containerimages), and the recovery process may use the labels to identify layersas immutable layers. As another example, the recovery process may beconfigured to use modification metadata associated with layers in thedataset to determine when layers were last modified, and any layers thathave not been modified within a time threshold may be identified asimmutable layers.

After the set of immutable layers 2212 has been identified, the recoveryprocess may copy the identified set of immutable layers 2212 to thesecond cluster 2202-2. The copying may be performed in any suitable waythat makes a copy of the set of immutable layers 2212 usable by thesecond cluster 2202-2 to deploy instances of the container applications2206 on nodes 2204-M through 2204-X of the second cluster 2202-2. As anexample, the recovery process may copy the immutable layers 2212 fromthe first storage system 2208-1, which provides storage services to thefirst cluster 2202-1, to a second storage system 2208-2 that isconfigured to provide storage services to the second cluster 2202-2. Thesecond storage system 2208-2 may be implemented similar to the firststorage system 2208-1 and may maintain and use a second dataset 2210-2to provide storage services to the second cluster 2202-2. The set ofimmutable layers 2212 may be copied from the first dataset 2210-1 of thefirst storage system 2208-1 to the second dataset 2210-2 as shown inFIG. 22A, after which the copied immutable layers 2212 may be used bythe second storage system 2208-2 to deploy instances of the containerapplications 2206 on nodes 2204-M through 2204-X of the second cluster2202-2.

In some implementations, the recovery process may avoid copyingduplicative immutable layers to the second cluster 2202-2. For example,as part of copying the set of immutable layers 2212 from the firstcluster 2202-1 to the second cluster 2202-1, the recovery process maycheck whether the second cluster 2202-2 already has a copy of animmutable layer before copying the immutable layer to the second cluster2202-2. If the second cluster 2202-2 already has a copy of an immutablelayer, that copy may be referenced and used by the recovery processwithout having to copy the immutable layer from the first cluster 2202-1to the second cluster 2202-1 as part of copying the set of immutablelayers 2212 from the first cluster 2202-1 to the second cluster 2202-1.

The recovery process may identify and copy the set of immutable layers2212 from the first cluster 2202-1 to the second cluster 2202-2 inadvance of a recovery event in preparation for such a recovery event.Accordingly, when a recovery event occurs after the set of immutablelayers 2212 has been identified and copied from the first cluster 2202-1to the second cluster 2202-2, the set of immutable layers 2212 isalready available for use by the second cluster 2202-2 to recover thecontainerized applications 2206 from the first cluster 2202-1 to thesecond cluster 2202-2. Because the set of immutable layers 2212 hasalready been copied in advance in preparation for the recovery event,the set of immutable layers 2212 need not be copied from the firstcluster 2202-1 to the second cluster 2202-1 as part of or in response tothe recovery event. Compared to traditional recovery techniques, thismay reduce downtime of the containerized applications 2206 beingrecovered and/or may make the recovery process more efficient.

The set of immutable layers 2212 may be identified and prioritized foradvance copying in this manner at least because the immutable layers2212 seldomly change and/or are not allowed to be changed by IO requestsassociated with normal operations of the containerized applications2206. Thus, the set of immutable layers 2212 may be unlikely to changebetween the time they are copied to the second cluster 2202-2 and a timewhen a recovery event occurs. In addition, if the set of immutablelayers 2212 in the first dataset 2210-1 of the first cluster 2202-1changes after being copied to the second cluster 2202-2, such as when animmutable layer of a container image is updated or added to the firstdataset 2210-1, the recovery process may detect the updated or newimmutable layer in the set of immutable layers 2212 in the first dataset2210-1 and copy the updated or new immutable layer to the second cluster2202-2 in advance of a recovery event.

After the set of immutable layers 2212 are copied to the second cluster,the recovery process may receive a recovery request to recover thecontainerized applications. The recovery request is represented asrecovery request 2224 in FIG. 22B. The recovery request may beassociated with a recovery event and may be received by the recoveryprocess from any suitable source. For example, an orchestrator of acontainer system or a storage system that provides storage services tothe container system may detect a recovery event, which event mayinclude a set of one or more conditions that make recovery of containerapplications from one cluster to another cluster desirable. Examples ofsuch conditions may include, without limitation, performance of acluster falling below or predicted to fall below a performancethreshold, utilization of a cluster reaching or predicted to reach autilization threshold, failure or predicted failure of one or morecompute nodes or storage resources of a cluster, load balancing orrebalancing, or any other condition(s). Upon detecting the recoveryevent, the orchestrator or storage system of the container system mayprovide a recovery request that may be received (e.g., detected) by therecovery process in any suitable way.

The request may include any information associated with the recoveryevent and/or recovery of containerized applications. For example, therecovery request may indicate a target compute location, such as asecond cluster, to which containerized applications will be recovered.

In response to the recovery request, the recovery process copies the setof mutable layers 1214 of the first dataset 1210-1 to the second cluster2202-2. The copying may be performed in any suitable way that makes acopy of the set of mutable layers 2214 usable by the second cluster2202-2 to deploy instances of the container applications 2206 on nodes2204-M through 2204-X of the second cluster 2202-2. As an example, therecovery process may identify and copy the mutable layers 2214 from thefirst dataset 2210-1 of the first storage system 2208-1 to the seconddataset 2210-2 of the second storage system 2208-2. The set of immutablelayers 2212 may be copied from the first dataset 2210-1 of the firststorage system 2208-1 to the second dataset 2210-2 is represented bydashed line 2226 shown in FIG. 22B.

After the mutable layers 2214 are copied to the second cluster 2202-2,the second cluster 2202-2 may use the copied set of immutable layers2212 and the copied set of mutable layers 2214 to recover thecontainerized applications 2206 on the second cluster 2202-2. This mayinclude the second cluster 2202-2 using the immutable layers 2212 todeploy the containerized applications 2206 on nodes 2204-M through2204-X of the second cluster 2202-2 and the containerized applications2206 accessing and using the mutable layers 2214 to start operations ofthe containerized applications 2206 at a recovery point in time at whichthe containerized applications 2206 existed on the first cluster 2202-1.To this end, the second storage system 2208-2 may make the copied set ofimmutable layers 2212 and the copied set of mutable layers 2214accessible to nodes 2204-M through 2204-X of the second cluster 2202-2,such as in any of the ways described herein. FIG. 22C illustrates thecontainerized applications 2206 recovered on nodes 2204-M through 2204-Xof the second cluster 2202-2.

As the containerized applications 2206 run on the second cluster 2202-2,the copy of the set of mutable layers 2214 in the second dataset 2210-2associated with the second cluster 2202-2 may be updated (e.g., writtento) by the containerized applications 2206. The updated set of mutablelayers 2214 is represented as updated mutable layers 2228 in FIG. 22D.

After the containerized applications 2206 have been recovered on thesecond cluster 2202-2 and have created the updated set of mutable layers2228 in the second dataset 2210-2, the recovery process may receive arequest to recover the containerized applications 2206 back from thesecond cluster 2202-2 to the first cluster 2202-1.

The recovery request may be associated with a recovery event and may bereceived by the recovery process from any suitable source. For example,an orchestrator of a container system or a storage system that providesstorage services to the container system may detect a recovery event,which event may include a set of one or more conditions that makerecovery of container applications back to the first cluster 2202-1desirable. Examples of such conditions may include, without limitation,performance of the first cluster 2202-1 being restored to a performancethreshold, utilization of the first cluster 2202-1 reaching or predictedto reach a utilization threshold, recovery of one or more compute nodesor storage resources of the first cluster 2202-1, increased capacity ofthe first cluster 2202-1, load balancing or rebalancing, or any othercondition(s). Upon detecting the recovery event, the orchestrator orstorage system of the container system may provide a recovery requestthat may be received (e.g., detected) by the recovery process in anysuitable way.

The recovery request is represented as recovery request 2230 in FIG.22D. The request may include any information associated with therecovery event and/or recovery of containerized applications.

In response to the recovery request, the recovery process identifies andcopies, from the second cluster 2202-2 to the first cluster 2202-1, anymutable layers that have changed on the second cluster 2202-2. In someimplementations, this includes identifying and copying only the mutablelayers that have been modified since the mutable layers 1214 were copiedfrom the first cluster 2202-1 to the second cluster 2202-2. The copyingof the updated mutable layers from the second dataset 2210-2 of thesecond storage system 2208-2 to the first dataset 2210-1 is representedby dashed line 2232 shown in FIG. 22D.

After the updated mutable layers 2228 are copied to the first cluster2202-1, the first cluster 2202-1 may use the set of immutable layers2212 and the copied set of updated mutable layers 2228 to recover thecontainerized applications 2206 back on the first cluster 2202-1. Thismay include the first cluster 2202-1 using the immutable layers 2212 todeploy the containerized applications 2206 on nodes 2204-1 through2204-K of the first cluster 2202-1 and the containerized applications2206 accessing and using the updated mutable layers 2228 to startoperations of the containerized applications 2206 at a recovery point intime at which the containerized applications 2206 existed on the secondcluster 2202-2. To this end, the first storage system 2208-1 may makethe set of immutable layers 2212 and the copied set of updated mutablelayers 2228 accessible to nodes 2204-1 through 2204-K of the firstcluster 2202-1, such as in any of the ways described herein. FIG. 22Eillustrates the containerized applications 2206 recovered on nodes2204-1 through 2204-K of the first cluster 2202-1.

FIGS. 23-25 illustrate example container recovery methods in accordancewith some embodiments of the present disclosure.

As shown in FIG. 23 , an example method 2300 includes: identifying, at2302, a set of immutable layers included in a dataset used to runcontainerized applications on a first cluster; copying, at 2304, the setof immutable layers to a second cluster in preparation for a recoveryevent; receiving, at 2306, a recovery request to recover thecontainerized applications; copying, at 2308 and in response to therecovery request, a set of mutable layers included in the dataset to thesecond cluster; and using, at 2310, the copied sets of immutable andmutable layers to recover the containerized applications on the secondcluster. In this example, operations of the method 2300 may be performedin any of the ways described herein, including as described in referenceto FIGS. 22A-22C.

As shown in FIG. 24 , an example method 2400 includes: detecting, at2402, an updated or new immutable layer in a dataset; and copying, at2404, the updated or new immutable layer to a second cluster inpreparation for a recovery event. In this example, operations of themethod 2400 may be performed in any of the ways described herein,including as described in reference to FIGS. 22A-22C.

As shown in FIG. 25 , an example method 2500 includes: receiving, at2502, a recovery request to recover containerized applications back froma second cluster to a first cluster; identifying and copying, at 2504and from the second cluster to the first cluster, any mutable layersthat have changed on the second cluster (e.g., any mutable layers thathave changed after a set of mutable layers was copied to the secondcluster); and using, at 2506, any copied mutable layers that changed onthe second cluster to recovery the containerized applications on thefirst cluster. In this example, operations of the method 2500 may beperformed in any of the ways described herein, including as described inreference to FIGS. 22D-22E.

In certain embodiments, a recovery process such as any of thosedescribed herein may be configured to prioritize more frequently usedlayers over less frequently used layers for one or more of the recoveryoperations described herein. As an example, a copying of immutablelayers may include the recovery process prioritizing more frequentlyused immutable layers over less frequently used immutable layers, suchas by copying more frequently used immutable layers first. As anotherexample, a copying of mutable layers may include the recovery processprioritizing more frequently used mutable layers over less frequentlyused mutable layers, such as by copying more frequently used mutablelayers first.

While certain illustrative examples have been described for recoveringcontainerized applications from one cluster to another cluster, one ormore of the operations described herein may be applied to recovercontainerized applications from any first compute location and resourcesto any second compute location and resources. For example, containerizedapplications may be recovered, in any of the ways described herein, fromone node to another node in a same cluster.

As mentioned, one or more of the recovery operations described hereinmay be performed by a container-aware storage system, which may use itsawareness of containers to optimize one or more aspects of disasterrecovery of the storage system. To illustrate an example, thecontainer-aware storage system may determine usage of specific layers ofdata and/or types of layers of data, such as immutable layers, containerimage layers, mutable layers, volume layers, etc., and utilize the usageand/or types of layers to prioritize one or more disaster recoveryoperations. In some implementations, this may include identifyingmutable layers (layers that store mutable data such as persistent databeing written by containerized applications) and immutable layers(layers that store immutable container image layers) and processing thedifferent types of layers differently and/or in a prioritized manner fordisaster recovery purposes, including in any of the ways describedherein.

In some examples, the storage system may be configured to prepare abackup copy of data that may be used to recover from a disaster, such asby using the backup copy of the data to recover containerizedapplications from one compute location to another compute location. Thestorage system may preload the backup with immutable layers that areunlikely to change or that change less often that mutable data. Forexample, container image layers may be treated as immutable layers thatare only modified when a containerized application is updated. Thestorage system may preload the backup with such immutable layers inadvance in preparation for a recovery event.

In some implementations, a primary cluster of nodes runningcontainerized applications prepares a secondary cluster for recovery.The preparation includes the storage system identifying immutable layersof container images and copying them over to the secondary cluster inadvance. Then when a recovery event occurs and containerizedapplications are to be recovered from the primary cluster to thesecondary cluster, a recovery process may copy over mutable layers tothe second cluster to get things running on the secondary cluster. Whenthe primary cluster comes back up, it already has the immutable layersand old mutable layers and can focus on looking for mutable layers thathave changed and copying those over from the secondary cluster. This mayallow the movement of running containerized applications from theprimary cluster to the secondary cluster and from the secondary clusterback to the primary cluster to be performed in an optimized manner.

In the preceding description, various illustrative embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe scope of the invention as set forth in the claims that follow. Forexample, certain features of one embodiment described herein may becombined with or substituted for features of another embodimentdescribed herein. The description and drawings are accordingly to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: identifying, by a recoveryprocess, a set of immutable layers of container images included in adataset used by a container system to run containerized applications ona first cluster; copying, by the recovery process, the set of immutablelayers of container images to a second cluster; receiving, by therecovery process after the set of immutable layers of container imagesare copied to the second cluster, a recovery request to recover thecontainerized applications; and copying, by the recovery process inresponse to the recovery request, a set of mutable layers included inthe dataset to the second cluster, the second cluster configured to usethe copied set of immutable layers and the copied set of mutable layersto recover the containerized applications on the second cluster.
 2. Themethod of claim 1, wherein the set of immutable layers of the containerimages comprises container image layers accessed and used by one or morecompute nodes of the first cluster to run the containerized applicationson the first cluster.
 3. The method of claim 1, wherein the set ofmutable layers are provided by a storage system as persistent storagefor the containerized applications running on the first cluster.
 4. Themethod of claim 1, wherein copying the set of immutable layers includedin the dataset to the second cluster comprises prioritizing morefrequently used immutable layers over less frequently used immutablelayers.
 5. The method of claim 1, wherein copying the set of mutablelayers included in the dataset to the second cluster comprisesprioritizing more frequently used mutable layers over less frequentlyused mutable layers.
 6. The method of claim 1, wherein copying the setof mutable layers included in the dataset to the second cluster inresponse to the recovery request comprises identifying and copying onlymutable layers in the set of mutable layers that have changed since theset of mutable layers was last copied to the second cluster.
 7. Themethod of claim 1, wherein the set of immutable layers comprises onlyunique immutable layers in the dataset.
 8. The method of claim 1,wherein the dataset is managed by a container-aware storage system thatprovides the immutable layers and the mutable layers for use by thecontainer system to run the containerized applications on the firstcluster.
 9. The method of claim 8, wherein the container-aware storagesystem manages and provides the immutable layers and the mutable layersas volumes.
 10. The method of claim 8, wherein the container-awarestorage system comprises containerized instances of the container-awarestorage system running on compute nodes of the first cluster.
 11. Themethod of claim 8, wherein the recovery process is included in thecontainer-aware storage system.
 12. The method of claim 1, furthercomprising: detecting an updated immutable layer in the set of immutablelayers included in the dataset; and copying the updated immutable layerto the second cluster.
 13. The method of claim 1, further comprising:detecting a new immutable layer in the set of immutable layers includedin the dataset; and copying the new immutable layer to the secondcluster.
 14. The method of claim 1, further comprising: receiving arecovery request to recover the containerized applications back to thefirst cluster; and identifying and copying, from the second cluster tothe first cluster, any of the mutable layers that have changed on thesecond cluster.
 15. A system comprising: one or more memories storingcomputer-executable instructions; and one or more processors to executethe computer-executable instructions to: identify a set of immutablelayers of container images included in a dataset used by a containersystem to run containerized applications on a first cluster; copy theset of immutable layers of container images to a second cluster;receive, after the set of immutable layers of container images arecopied to the second cluster, a recovery request to recover thecontainerized applications; and copy, in response to the recoveryrequest, a set of mutable layers included in the dataset to the secondcluster, the second cluster configured to use the copied set ofimmutable layers and the copied set of mutable layers to recover thecontainerized applications on the second cluster.
 16. The system ofclaim 15, wherein: the set of immutable layers of the container imagescomprises container image layers accessed and used by one or morecompute nodes of the first cluster to run the containerized applicationson the first cluster; and the set of mutable layers are provided by astorage system as persistent storage for the containerized applicationsrunning on the first cluster.
 17. The system of claim 15, wherein thedataset is managed by a container-aware storage system that provides theimmutable layers and the mutable layers for use by the container systemto run the containerized applications on the first cluster.
 18. Thesystem of claim 17, wherein the container-aware storage system managesand provides the immutable layers and the mutable layers as volumes. 19.The system of claim 15, wherein the one or more processors are furtherconfigured to execute the computer-executable instructions to: receive arecovery request to recover the containerized applications back to thefirst cluster; and identify and copy, from the second cluster to thefirst cluster, any of the mutable layers that have changed on the secondcluster.
 20. A non-transitory, computer-readable medium storing computerinstructions that, when executed, direct one or more processors of oneor more computing devices to: identify a set of immutable layers ofcontainer images included in a dataset used by a container system to runcontainerized applications on a first cluster; copy the set of immutablelayers of container images to a second cluster; receive, after the setof immutable layers of container images are copied to the secondcluster, a recovery request to recover the containerized applications;and copy, in response to the recovery request, a set of mutable layersincluded in the dataset to the second cluster, the second clusterconfigured to use the copied set of immutable layers and the copied setof mutable layers to recover the containerized applications on thesecond cluster.